Overview

Dataset statistics

 TrainTest
Number of variables8989
Number of observations2688111521
Missing cells00
Missing cells (%)0.0%0.0%
Total size in memory18.4 MiB7.9 MiB
Average record size in memory717.0 B717.0 B

Variable types

 TrainTest
Numeric7573
Categorical1315
Boolean11

Alerts

TrainTest
campaign_id has constant value "1"campaign_id has constant value "1"Constant
direct_mail_flag has constant value "True"direct_mail_flag has constant value "True"Constant
AGE is highly overall correlated with MARITALSTATUS AGE is highly overall correlated with MARITALSTATUSHigh correlation
Age_Newest_TL is highly overall correlated with Tot_Active_TL and 8 other fields Age_Newest_TL is highly overall correlated with Tot_Active_TL and 6 other fieldsHigh correlation
Age_Oldest_TL is highly overall correlated with Secured_TL and 6 other fields Age_Oldest_TL is highly overall correlated with Secured_TL and 6 other fieldsHigh correlation
CC_Flag is highly overall correlated with CC_utilization and 1 other fields CC_Flag is highly overall correlated with CC_utilization and 1 other fieldsHigh correlation
CC_TL is highly overall correlated with CC_enq and 2 other fields CC_TL is highly overall correlated with CC_enq and 2 other fieldsHigh correlation
CC_enq is highly overall correlated with CC_TL and 12 other fields CC_enq is highly overall correlated with CC_TL and 12 other fieldsHigh correlation
CC_enq_L12m is highly overall correlated with CC_TL and 12 other fields CC_enq_L12m is highly overall correlated with CC_TL and 12 other fieldsHigh correlation
CC_enq_L6m is highly overall correlated with CC_enq and 10 other fields CC_enq_L6m is highly overall correlated with CC_enq and 10 other fieldsHigh correlation
CC_utilization is highly overall correlated with CC_Flag and 3 other fields CC_utilization is highly overall correlated with CC_Flag and 3 other fieldsHigh correlation
Consumer_TL is highly overall correlated with Total_TL_opened_L12M and 2 other fields Consumer_TL is highly overall correlated with Total_TL_opened_L12M and 2 other fieldsHigh correlation
Credit_Score is highly overall correlated with enq_L3m Credit_Score is highly overall correlated with enq_L3mHigh correlation
EDUCATION is highly overall correlated with PROSPECTID EDUCATION is highly overall correlated with PROSPECTIDHigh correlation
GENDER is highly overall correlated with PROSPECTID GENDER is highly overall correlated with PROSPECTIDHigh correlation
GL_Flag is highly overall correlated with Home_TL and 1 other fields GL_Flag is highly overall correlated with Home_TL and 1 other fieldsHigh correlation
Gold_TL is highly overall correlated with Secured_TL and 1 other fields Gold_TL is highly overall correlated with Secured_TL and 2 other fieldsHigh correlation
HL_Flag is highly overall correlated with PROSPECTID HL_Flag is highly overall correlated with PROSPECTIDHigh correlation
Home_TL is highly overall correlated with GL_Flag Home_TL is highly overall correlated with GL_FlagHigh correlation
MARITALSTATUS is highly overall correlated with AGE and 1 other fields MARITALSTATUS is highly overall correlated with AGE and 1 other fieldsHigh correlation
PL_Flag is highly overall correlated with PL_utilization and 1 other fields PL_Flag is highly overall correlated with PL_utilization and 1 other fieldsHigh correlation
PL_TL is highly overall correlated with PL_enq and 2 other fields PL_TL is highly overall correlated with PL_enq and 1 other fieldsHigh correlation
PL_enq is highly overall correlated with CC_enq and 13 other fields PL_enq is highly overall correlated with CC_enq and 12 other fieldsHigh correlation
PL_enq_L12m is highly overall correlated with CC_enq and 10 other fields PL_enq_L12m is highly overall correlated with CC_enq and 10 other fieldsHigh correlation
PL_enq_L6m is highly overall correlated with CC_enq and 10 other fields PL_enq_L6m is highly overall correlated with CC_enq and 10 other fieldsHigh correlation
PL_utilization is highly overall correlated with PL_Flag and 2 other fields PL_utilization is highly overall correlated with PL_Flag and 2 other fieldsHigh correlation
PROSPECTID is highly overall correlated with CC_Flag and 11 other fields PROSPECTID is highly overall correlated with CC_Flag and 13 other fieldsHigh correlation
Secured_TL is highly overall correlated with Age_Oldest_TL and 3 other fields Secured_TL is highly overall correlated with Age_Oldest_TL and 3 other fieldsHigh correlation
Tot_Active_TL is highly overall correlated with Age_Newest_TL and 7 other fields Tot_Active_TL is highly overall correlated with Age_Newest_TL and 7 other fieldsHigh correlation
Tot_Closed_TL is highly overall correlated with Age_Oldest_TL and 9 other fields Tot_Closed_TL is highly overall correlated with Age_Oldest_TL and 9 other fieldsHigh correlation
Tot_Missed_Pmnt is highly overall correlated with Age_Newest_TL and 2 other fields Tot_Missed_Pmnt is highly overall correlated with Age_Newest_TL and 3 other fieldsHigh correlation
Tot_TL_closed_L12M is highly overall correlated with Tot_Closed_TL and 4 other fields Tot_TL_closed_L12M is highly overall correlated with Tot_Closed_TL and 4 other fieldsHigh correlation
Tot_TL_closed_L6M is highly overall correlated with Tot_Closed_TL and 3 other fields Tot_TL_closed_L6M is highly overall correlated with Tot_Closed_TL and 3 other fieldsHigh correlation
Total_TL is highly overall correlated with Age_Oldest_TL and 6 other fields Total_TL is highly overall correlated with Age_Oldest_TL and 7 other fieldsHigh correlation
Total_TL_opened_L12M is highly overall correlated with Age_Newest_TL and 9 other fields Total_TL_opened_L12M is highly overall correlated with Age_Newest_TL and 10 other fieldsHigh correlation
Total_TL_opened_L6M is highly overall correlated with Age_Newest_TL and 5 other fields Total_TL_opened_L6M is highly overall correlated with Age_Newest_TL and 5 other fieldsHigh correlation
Unsecured_TL is highly overall correlated with Consumer_TL and 6 other fields Unsecured_TL is highly overall correlated with Consumer_TL and 6 other fieldsHigh correlation
enq_L12m is highly overall correlated with Age_Newest_TL and 12 other fields enq_L12m is highly overall correlated with CC_enq and 11 other fieldsHigh correlation
enq_L3m is highly overall correlated with CC_enq and 9 other fields enq_L3m is highly overall correlated with CC_enq and 9 other fieldsHigh correlation
enq_L6m is highly overall correlated with CC_enq and 10 other fields enq_L6m is highly overall correlated with CC_enq and 10 other fieldsHigh correlation
first_prod_enq2 is highly overall correlated with PROSPECTID first_prod_enq2 is highly overall correlated with PROSPECTIDHigh correlation
last_prod_enq2 is highly overall correlated with PROSPECTID last_prod_enq2 is highly overall correlated with PROSPECTIDHigh correlation
max_delinquency_level is highly overall correlated with max_deliq_12mts and 9 other fields max_delinquency_level is highly overall correlated with max_deliq_12mts and 9 other fieldsHigh correlation
max_deliq_12mts is highly overall correlated with max_delinquency_level and 7 other fields max_deliq_12mts is highly overall correlated with max_delinquency_level and 7 other fieldsHigh correlation
max_deliq_6mts is highly overall correlated with max_deliq_12mts and 2 other fields max_deliq_6mts is highly overall correlated with max_deliq_12mts and 2 other fieldsHigh correlation
max_recent_level_of_deliq is highly overall correlated with max_delinquency_level and 9 other fields max_recent_level_of_deliq is highly overall correlated with max_delinquency_level and 9 other fieldsHigh correlation
max_unsec_exposure_inPct is highly overall correlated with PL_TL and 4 other fields max_unsec_exposure_inPct is highly overall correlated with PL_utilization and 2 other fieldsHigh correlation
num_dbt is highly overall correlated with num_dbt_6mts num_dbt is highly overall correlated with num_dbt_12mts and 1 other fieldsHigh correlation
num_dbt_12mts is highly overall correlated with num_dbt_6mts num_dbt_12mts is highly overall correlated with num_dbt and 1 other fieldsHigh correlation
num_dbt_6mts is highly overall correlated with PROSPECTID and 2 other fields num_dbt_6mts is highly overall correlated with PROSPECTID and 2 other fieldsHigh correlation
num_deliq_12mts is highly overall correlated with max_delinquency_level and 9 other fields num_deliq_12mts is highly overall correlated with max_delinquency_level and 9 other fieldsHigh correlation
num_deliq_6_12mts is highly overall correlated with max_delinquency_level and 6 other fields num_deliq_6_12mts is highly overall correlated with max_delinquency_level and 6 other fieldsHigh correlation
num_deliq_6mts is highly overall correlated with max_deliq_12mts and 3 other fields num_deliq_6mts is highly overall correlated with max_deliq_12mts and 3 other fieldsHigh correlation
num_lss is highly overall correlated with num_lss_6mts num_lss is highly overall correlated with num_lss_12mts and 1 other fieldsHigh correlation
num_lss_12mts is highly overall correlated with num_lss_6mts num_lss_12mts is highly overall correlated with PROSPECTID and 2 other fieldsHigh correlation
num_lss_6mts is highly overall correlated with PROSPECTID and 2 other fields num_lss_6mts is highly overall correlated with PROSPECTID and 2 other fieldsHigh correlation
num_std is highly overall correlated with num_std_12mts and 1 other fields num_std is highly overall correlated with num_std_12mts and 1 other fieldsHigh correlation
num_std_12mts is highly overall correlated with num_std and 1 other fields num_std_12mts is highly overall correlated with num_std and 1 other fieldsHigh correlation
num_std_6mts is highly overall correlated with num_std and 1 other fields num_std_6mts is highly overall correlated with num_std and 1 other fieldsHigh correlation
num_sub is highly overall correlated with num_sub_12mts num_sub is highly overall correlated with num_sub_6mtsHigh correlation
num_sub_12mts is highly overall correlated with num_sub and 1 other fields num_sub_12mts is highly overall correlated with num_sub_6mtsHigh correlation
num_sub_6mts is highly overall correlated with num_sub_12mts num_sub_6mts is highly overall correlated with PROSPECTID and 2 other fieldsHigh correlation
num_times_30p_dpd is highly overall correlated with max_delinquency_level and 6 other fields num_times_30p_dpd is highly overall correlated with max_delinquency_level and 6 other fieldsHigh correlation
num_times_60p_dpd is highly overall correlated with max_delinquency_level and 5 other fields num_times_60p_dpd is highly overall correlated with max_delinquency_level and 5 other fieldsHigh correlation
num_times_delinquent is highly overall correlated with max_delinquency_level and 10 other fields num_times_delinquent is highly overall correlated with max_delinquency_level and 10 other fieldsHigh correlation
pct_CC_enq_L6m_of_L12m is highly overall correlated with CC_enq and 3 other fields pct_CC_enq_L6m_of_L12m is highly overall correlated with CC_enq and 3 other fieldsHigh correlation
pct_CC_enq_L6m_of_ever is highly overall correlated with CC_enq and 3 other fields pct_CC_enq_L6m_of_ever is highly overall correlated with CC_enq and 3 other fieldsHigh correlation
pct_PL_enq_L6m_of_L12m is highly overall correlated with PL_enq and 5 other fields pct_PL_enq_L6m_of_L12m is highly overall correlated with PL_enq and 5 other fieldsHigh correlation
pct_PL_enq_L6m_of_ever is highly overall correlated with PL_enq and 5 other fields pct_PL_enq_L6m_of_ever is highly overall correlated with PL_enq and 5 other fieldsHigh correlation
pct_active_tl is highly overall correlated with Age_Oldest_TL and 3 other fields pct_active_tl is highly overall correlated with Age_Oldest_TL and 3 other fieldsHigh correlation
pct_closed_tl is highly overall correlated with Age_Oldest_TL and 3 other fields pct_closed_tl is highly overall correlated with Age_Oldest_TL and 3 other fieldsHigh correlation
pct_currentBal_all_TL is highly overall correlated with Age_Newest_TLAlert not present in this datasetHigh correlation
pct_of_active_TLs_ever is highly overall correlated with Age_Oldest_TL and 3 other fields pct_of_active_TLs_ever is highly overall correlated with Age_Oldest_TL and 3 other fieldsHigh correlation
pct_opened_TLs_L6m_of_L12m is highly overall correlated with Age_Newest_TL and 3 other fields pct_opened_TLs_L6m_of_L12m is highly overall correlated with Age_Newest_TL and 3 other fieldsHigh correlation
pct_tl_closed_L12M is highly overall correlated with Tot_Closed_TL and 3 other fields pct_tl_closed_L12M is highly overall correlated with Tot_Closed_TL and 3 other fieldsHigh correlation
pct_tl_closed_L6M is highly overall correlated with Tot_TL_closed_L12M and 2 other fields pct_tl_closed_L6M is highly overall correlated with Tot_TL_closed_L12M and 2 other fieldsHigh correlation
pct_tl_open_L12M is highly overall correlated with Age_Newest_TL and 3 other fields pct_tl_open_L12M is highly overall correlated with Age_Newest_TL and 3 other fieldsHigh correlation
pct_tl_open_L6M is highly overall correlated with Age_Newest_TL and 4 other fields pct_tl_open_L6M is highly overall correlated with Age_Newest_TL and 4 other fieldsHigh correlation
recent_level_of_deliq is highly overall correlated with max_delinquency_level and 9 other fields recent_level_of_deliq is highly overall correlated with max_delinquency_level and 9 other fieldsHigh correlation
response_flag is highly overall correlated with PROSPECTID response_flag is highly overall correlated with PROSPECTIDHigh correlation
time_since_first_deliquency is highly overall correlated with max_delinquency_level and 8 other fields time_since_first_deliquency is highly overall correlated with max_delinquency_level and 8 other fieldsHigh correlation
time_since_recent_deliquency is highly overall correlated with max_delinquency_level and 6 other fields time_since_recent_deliquency is highly overall correlated with max_delinquency_level and 6 other fieldsHigh correlation
tot_enq is highly overall correlated with CC_enq and 11 other fields tot_enq is highly overall correlated with CC_enq and 11 other fieldsHigh correlation
response_flag is highly imbalanced (67.9%)response_flag is highly imbalanced (69.8%)Imbalance
num_dbt_6mts is highly imbalanced (99.8%)num_dbt_6mts is highly imbalanced (99.8%)Imbalance
num_lss_6mts is highly imbalanced (99.9%)num_lss_6mts is highly imbalanced (99.8%)Imbalance
GL_Flag is highly imbalanced (68.9%)GL_Flag is highly imbalanced (68.4%)Imbalance
num_sub is highly skewed (γ1 = 22.11940752)Alert not present in this datasetSkewed
num_sub_6mts is highly skewed (γ1 = 47.08338708)Alert not present in this datasetSkewed
num_sub_12mts is highly skewed (γ1 = 32.33341137)num_sub_12mts is highly skewed (γ1 = 36.14461754)Skewed
num_dbt is highly skewed (γ1 = 34.12793416)num_dbt is highly skewed (γ1 = 36.47892137)Skewed
num_dbt_12mts is highly skewed (γ1 = 56.20134414)num_dbt_12mts is highly skewed (γ1 = 48.56599513)Skewed
num_lss is highly skewed (γ1 = 68.3217958)num_lss is highly skewed (γ1 = 42.50113193)Skewed
num_lss_12mts is highly skewed (γ1 = 91.25653726)Alert not present in this datasetSkewed
pct_currentBal_all_TL is highly skewed (γ1 = -29.8782903)pct_currentBal_all_TL is highly skewed (γ1 = -27.6634631)Skewed
Age_Oldest_TL is highly skewed (γ1 = -39.71192992)Age_Oldest_TL is highly skewed (γ1 = -29.71586378)Skewed
Age_Newest_TL is highly skewed (γ1 = -39.72490923)Age_Newest_TL is highly skewed (γ1 = -29.72124896)Skewed
PROSPECTID has unique valuesPROSPECTID has unique valuesUnique
num_times_delinquent has 18396 (68.4%) zerosnum_times_delinquent has 7906 (68.6%) zerosZeros
max_recent_level_of_deliq has 18396 (68.4%) zerosmax_recent_level_of_deliq has 7906 (68.6%) zerosZeros
num_deliq_6mts has 24047 (89.5%) zerosnum_deliq_6mts has 10269 (89.1%) zerosZeros
num_deliq_12mts has 22082 (82.1%) zerosnum_deliq_12mts has 9429 (81.8%) zerosZeros
num_deliq_6_12mts has 23403 (87.1%) zerosnum_deliq_6_12mts has 9974 (86.6%) zerosZeros
max_deliq_6mts has 17922 (66.7%) zerosmax_deliq_6mts has 7633 (66.3%) zerosZeros
max_deliq_12mts has 16962 (63.1%) zerosmax_deliq_12mts has 7201 (62.5%) zerosZeros
num_times_30p_dpd has 22496 (83.7%) zerosnum_times_30p_dpd has 9667 (83.9%) zerosZeros
num_times_60p_dpd has 24185 (90.0%) zerosnum_times_60p_dpd has 10396 (90.2%) zerosZeros
num_std has 16577 (61.7%) zerosnum_std has 7061 (61.3%) zerosZeros
num_std_6mts has 19529 (72.6%) zerosnum_std_6mts has 8393 (72.8%) zerosZeros
num_std_12mts has 18658 (69.4%) zerosnum_std_12mts has 7999 (69.4%) zerosZeros
num_sub has 26562 (98.8%) zerosnum_sub has 11384 (98.8%) zerosZeros
num_sub_6mts has 26856 (99.9%) zerosAlert not present in this datasetZeros
num_sub_12mts has 26800 (99.7%) zerosnum_sub_12mts has 11489 (99.7%) zerosZeros
num_dbt has 26790 (99.7%) zerosnum_dbt has 11489 (99.7%) zerosZeros
num_dbt_12mts has 26864 (99.9%) zerosnum_dbt_12mts has 11512 (99.9%) zerosZeros
num_lss has 26823 (99.8%) zerosnum_lss has 11501 (99.8%) zerosZeros
num_lss_12mts has 26868 (> 99.9%) zerosAlert not present in this datasetZeros
recent_level_of_deliq has 18396 (68.4%) zerosrecent_level_of_deliq has 7906 (68.6%) zerosZeros
CC_enq has 17558 (65.3%) zerosCC_enq has 7590 (65.9%) zerosZeros
CC_enq_L6m has 20703 (77.0%) zerosCC_enq_L6m has 8914 (77.4%) zerosZeros
CC_enq_L12m has 19454 (72.4%) zerosCC_enq_L12m has 8393 (72.8%) zerosZeros
PL_enq has 13144 (48.9%) zerosPL_enq has 5680 (49.3%) zerosZeros
PL_enq_L6m has 17559 (65.3%) zerosPL_enq_L6m has 7558 (65.6%) zerosZeros
PL_enq_L12m has 15334 (57.0%) zerosPL_enq_L12m has 6640 (57.6%) zerosZeros
enq_L12m has 4643 (17.3%) zerosenq_L12m has 2064 (17.9%) zerosZeros
enq_L6m has 7885 (29.3%) zerosenq_L6m has 3426 (29.7%) zerosZeros
enq_L3m has 10756 (40.0%) zerosenq_L3m has 4675 (40.6%) zerosZeros
pct_of_active_TLs_ever has 4077 (15.2%) zerospct_of_active_TLs_ever has 1682 (14.6%) zerosZeros
pct_opened_TLs_L6m_of_L12m has 15421 (57.4%) zerospct_opened_TLs_L6m_of_L12m has 6654 (57.8%) zerosZeros
pct_currentBal_all_TL has 5615 (20.9%) zerospct_currentBal_all_TL has 2362 (20.5%) zerosZeros
pct_PL_enq_L6m_of_L12m has 20545 (76.4%) zerospct_PL_enq_L6m_of_L12m has 8785 (76.3%) zerosZeros
pct_CC_enq_L6m_of_L12m has 23689 (88.1%) zerospct_CC_enq_L6m_of_L12m has 10141 (88.0%) zerosZeros
pct_PL_enq_L6m_of_ever has 20545 (76.4%) zerospct_PL_enq_L6m_of_ever has 8785 (76.3%) zerosZeros
pct_CC_enq_L6m_of_ever has 23689 (88.1%) zerospct_CC_enq_L6m_of_ever has 10141 (88.0%) zerosZeros
max_unsec_exposure_inPct has 275 (1.0%) zerosAlert not present in this datasetZeros
Tot_Closed_TL has 9440 (35.1%) zerosTot_Closed_TL has 4047 (35.1%) zerosZeros
Tot_Active_TL has 4077 (15.2%) zerosTot_Active_TL has 1682 (14.6%) zerosZeros
Total_TL_opened_L6M has 15421 (57.4%) zerosTotal_TL_opened_L6M has 6654 (57.8%) zerosZeros
Tot_TL_closed_L6M has 19593 (72.9%) zerosTot_TL_closed_L6M has 8371 (72.7%) zerosZeros
pct_tl_open_L6M has 15421 (57.4%) zerospct_tl_open_L6M has 6654 (57.8%) zerosZeros
pct_tl_closed_L6M has 19593 (72.9%) zerospct_tl_closed_L6M has 8371 (72.7%) zerosZeros
pct_active_tl has 4077 (15.2%) zerospct_active_tl has 1682 (14.6%) zerosZeros
pct_closed_tl has 9440 (35.1%) zerospct_closed_tl has 4047 (35.1%) zerosZeros
Total_TL_opened_L12M has 8956 (33.3%) zerosTotal_TL_opened_L12M has 3852 (33.4%) zerosZeros
Tot_TL_closed_L12M has 16310 (60.7%) zerosTot_TL_closed_L12M has 6996 (60.7%) zerosZeros
pct_tl_open_L12M has 8956 (33.3%) zerospct_tl_open_L12M has 3852 (33.4%) zerosZeros
pct_tl_closed_L12M has 16310 (60.7%) zerospct_tl_closed_L12M has 6996 (60.7%) zerosZeros
Tot_Missed_Pmnt has 17534 (65.2%) zerosTot_Missed_Pmnt has 7529 (65.4%) zerosZeros
Auto_TL has 15135 (56.3%) zerosAuto_TL has 6532 (56.7%) zerosZeros
CC_TL has 21879 (81.4%) zerosCC_TL has 9375 (81.4%) zerosZeros
Consumer_TL has 14347 (53.4%) zerosConsumer_TL has 6189 (53.7%) zerosZeros
Gold_TL has 19461 (72.4%) zerosGold_TL has 8398 (72.9%) zerosZeros
Home_TL has 25377 (94.4%) zerosHome_TL has 10863 (94.3%) zerosZeros
PL_TL has 21662 (80.6%) zerosPL_TL has 9343 (81.1%) zerosZeros
Secured_TL has 7585 (28.2%) zerosSecured_TL has 3270 (28.4%) zerosZeros
Unsecured_TL has 8454 (31.4%) zerosUnsecured_TL has 3604 (31.3%) zerosZeros
Other_TL has 15127 (56.3%) zerosOther_TL has 6457 (56.0%) zerosZeros
Alert not present in this datasetnum_sub_6mts is highly imbalanced (99.6%)Imbalance
Alert not present in this datasetnum_lss_12mts is highly imbalanced (99.8%)Imbalance
Alert not present in this datasetNETMONTHLYINCOME is highly skewed (γ1 = 55.07041449)Skewed
Alert not present in this datasetCC_utilization has 119 (1.0%) zerosZeros

Reproduction

 TrainTest
Analysis started2026-01-18 06:56:58.4268102026-01-18 06:57:17.502660
Analysis finished2026-01-18 06:57:17.4996542026-01-18 06:57:29.541627
Duration19.07 seconds12.04 seconds
Software versionydata-profiling vv4.18.1ydata-profiling vv4.18.1
Download configurationconfig.jsonconfig.json

Variables

PROSPECTID
Real number (ℝ)

 TrainTest
Distinct2688111521
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean288005.49286322.41
 TrainTest
Minimum506
Maximum575916575868
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:30.003763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum506
5-th percentile2796628234
Q1143747144902
median288881286228
Q3430581429301
95-th percentile548031543919
Maximum575916575868
Range575866575862
Interquartile range (IQR)286834284399

Descriptive statistics

 TrainTest
Standard deviation166320.8165516.03
Coefficient of variation (CV)0.577491750.57807569
Kurtosis-1.1947889-1.1922891
Mean288005.49286322.41
Median Absolute Deviation (MAD)143477142032
Skewness-0.00305894240.00068900159
Sum7.7418756 × 1093.2987205 × 109
Variance2.7662607 × 10102.7395555 × 1010
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:30.105591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5245781
 
< 0.1%
5646231
 
< 0.1%
1278391
 
< 0.1%
2412681
 
< 0.1%
1264101
 
< 0.1%
1051701
 
< 0.1%
1994261
 
< 0.1%
4689471
 
< 0.1%
5391571
 
< 0.1%
4748701
 
< 0.1%
Other values (26871)26871
> 99.9%
ValueCountFrequency (%)
3842531
 
< 0.1%
3599881
 
< 0.1%
4021501
 
< 0.1%
2471711
 
< 0.1%
2360381
 
< 0.1%
3356771
 
< 0.1%
2838931
 
< 0.1%
5688741
 
< 0.1%
5259551
 
< 0.1%
4863071
 
< 0.1%
Other values (11511)11511
99.9%
ValueCountFrequency (%)
501
< 0.1%
601
< 0.1%
1021
< 0.1%
1091
< 0.1%
1391
< 0.1%
1431
< 0.1%
1451
< 0.1%
1481
< 0.1%
1701
< 0.1%
1741
< 0.1%
ValueCountFrequency (%)
61
< 0.1%
671
< 0.1%
1291
< 0.1%
3711
< 0.1%
3831
< 0.1%
4931
< 0.1%
5491
< 0.1%
5841
< 0.1%
6781
< 0.1%
7371
< 0.1%
ValueCountFrequency (%)
61
< 0.1%
671
< 0.1%
1291
< 0.1%
3711
< 0.1%
3831
< 0.1%
4931
< 0.1%
5491
< 0.1%
5841
< 0.1%
6781
< 0.1%
7371
< 0.1%
ValueCountFrequency (%)
501
< 0.1%
601
< 0.1%
1021
< 0.1%
1091
< 0.1%
1391
< 0.1%
1431
< 0.1%
1451
< 0.1%
1481
< 0.1%
1701
< 0.1%
1741
< 0.1%

campaign_id
Categorical

 TrainTest
Distinct11
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
1
26881 
1
11521 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters11
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
126881
100.0%
ValueCountFrequency (%)
111521
100.0%

Length

2026-01-17T22:57:30.188138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:30.225268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:30.243936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
126881
100.0%
ValueCountFrequency (%)
111521
100.0%

Most occurring characters

ValueCountFrequency (%)
126881
100.0%
ValueCountFrequency (%)
111521
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
126881
100.0%
ValueCountFrequency (%)
111521
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
126881
100.0%
ValueCountFrequency (%)
111521
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
126881
100.0%
ValueCountFrequency (%)
111521
100.0%

response_flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
25314 
1
 
1567
0
10901 
1
 
620

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
025314
94.2%
11567
 
5.8%
ValueCountFrequency (%)
010901
94.6%
1620
 
5.4%

Length

2026-01-17T22:57:30.285956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:30.327588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:30.351110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
025314
94.2%
11567
 
5.8%
ValueCountFrequency (%)
010901
94.6%
1620
 
5.4%

Most occurring characters

ValueCountFrequency (%)
025314
94.2%
11567
 
5.8%
ValueCountFrequency (%)
010901
94.6%
1620
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
025314
94.2%
11567
 
5.8%
ValueCountFrequency (%)
010901
94.6%
1620
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
025314
94.2%
11567
 
5.8%
ValueCountFrequency (%)
010901
94.6%
1620
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
025314
94.2%
11567
 
5.8%
ValueCountFrequency (%)
010901
94.6%
1620
 
5.4%
 TrainTest
Distinct11
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size236.3 KiB101.3 KiB
True
26881 
True
11521 
ValueCountFrequency (%)
True26881
100.0%
ValueCountFrequency (%)
True11521
100.0%

Train

2026-01-17T22:57:30.372755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:30.390285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

time_since_recent_payment
Real number (ℝ)

 TrainTest
Distinct17441286
Distinct (%)6.5%11.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-7651.1062-7793.6235
 TrainTest
Minimum-99999-99999
Maximum55406065
Zeros00
Zeros (%)0.0%0.0%
Negative2114922
Negative (%)7.9%8.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:30.456901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q14444
median6666
Q3134129
95-th percentile10471000
Maximum55406065
Range105539106064
Interquartile range (IQR)9085

Descriptive statistics

 TrainTest
Standard deviation26984.04127199.509
Coefficient of variation (CV)-3.5268157-3.4899697
Kurtosis7.79730717.5814508
Mean-7651.1062-7793.6235
Median Absolute Deviation (MAD)2828
Skewness-3.1293803-3.0946229
Sum-2.0566939 × 108-89790336
Variance7.2813849 × 1087.3981331 × 108
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:30.560229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999992114
 
7.9%
50366
 
1.4%
54357
 
1.3%
43350
 
1.3%
46344
 
1.3%
59336
 
1.2%
49330
 
1.2%
52323
 
1.2%
53311
 
1.2%
63310
 
1.2%
Other values (1734)21740
80.9%
ValueCountFrequency (%)
-99999922
 
8.0%
51152
 
1.3%
49151
 
1.3%
47149
 
1.3%
59147
 
1.3%
50145
 
1.3%
61140
 
1.2%
43140
 
1.2%
46136
 
1.2%
53136
 
1.2%
Other values (1276)9303
80.7%
ValueCountFrequency (%)
-999992114
7.9%
31
 
< 0.1%
41
 
< 0.1%
56
 
< 0.1%
68
 
< 0.1%
710
 
< 0.1%
86
 
< 0.1%
97
 
< 0.1%
1013
 
< 0.1%
1112
 
< 0.1%
ValueCountFrequency (%)
-99999922
8.0%
42
 
< 0.1%
53
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
88
 
0.1%
92
 
< 0.1%
106
 
0.1%
113
 
< 0.1%
125
 
< 0.1%
ValueCountFrequency (%)
-99999922
3.4%
42
 
< 0.1%
53
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
88
 
< 0.1%
92
 
< 0.1%
106
 
< 0.1%
113
 
< 0.1%
125
 
< 0.1%
ValueCountFrequency (%)
-999992114
18.3%
31
 
< 0.1%
41
 
< 0.1%
56
 
0.1%
68
 
0.1%
710
 
0.1%
86
 
0.1%
97
 
0.1%
1013
 
0.1%
1112
 
0.1%

time_since_first_deliquency
Real number (ℝ)

 TrainTest
Distinct3737
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-68427.882-68615.484
 TrainTest
Minimum-99999-99999
Maximum3535
Zeros2610
Zeros (%)0.1%0.1%
Negative183967906
Negative (%)68.4%68.6%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:30.664903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q1-99999-99999
median-99999-99999
Q398
95-th percentile3334
Maximum3535
Range100034100034
Interquartile range (IQR)100008100007

Descriptive statistics

 TrainTest
Standard deviation46487.24746413.541
Coefficient of variation (CV)-0.67936118-0.67642956
Kurtosis-1.3707287-1.3558212
Mean-68427.882-68615.484
Median Absolute Deviation (MAD)00
Skewness0.793330440.80275411
Sum-1.8394099 × 109-7.9051899 × 108
Variance2.1610641 × 1092.1542168 × 109
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:30.765607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
-9999918396
68.4%
351001
 
3.7%
34326
 
1.2%
33291
 
1.1%
9272
 
1.0%
6267
 
1.0%
29264
 
1.0%
31264
 
1.0%
8263
 
1.0%
5255
 
0.9%
Other values (27)5282
 
19.6%
ValueCountFrequency (%)
-999997906
68.6%
35445
 
3.9%
7143
 
1.2%
34142
 
1.2%
33124
 
1.1%
29122
 
1.1%
6120
 
1.0%
9119
 
1.0%
5109
 
0.9%
31106
 
0.9%
Other values (27)2185
 
19.0%
ValueCountFrequency (%)
-9999918396
68.4%
026
 
0.1%
157
 
0.2%
2127
 
0.5%
3211
 
0.8%
4217
 
0.8%
5255
 
0.9%
6267
 
1.0%
7242
 
0.9%
8263
 
1.0%
ValueCountFrequency (%)
-999997906
68.6%
010
 
0.1%
119
 
0.2%
253
 
0.5%
386
 
0.7%
4100
 
0.9%
5109
 
0.9%
6120
 
1.0%
7143
 
1.2%
8102
 
0.9%
ValueCountFrequency (%)
-999997906
29.4%
010
 
< 0.1%
119
 
0.1%
253
 
0.2%
386
 
0.3%
4100
 
0.4%
5109
 
0.4%
6120
 
0.4%
7143
 
0.5%
8102
 
0.4%
ValueCountFrequency (%)
-9999918396
159.7%
026
 
0.2%
157
 
0.5%
2127
 
1.1%
3211
 
1.8%
4217
 
1.9%
5255
 
2.2%
6267
 
2.3%
7242
 
2.1%
8263
 
2.3%
 TrainTest
Distinct3737
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-68430.414-68618.029
 TrainTest
Minimum-99999-99999
Maximum3535
Zeros9035
Zeros (%)0.3%0.3%
Negative183967906
Negative (%)68.4%68.6%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:30.853950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q1-99999-99999
median-99999-99999
Q333
95-th percentile2424
Maximum3535
Range100034100034
Interquartile range (IQR)100002100002

Descriptive statistics

 TrainTest
Standard deviation46483.51846409.777
Coefficient of variation (CV)-0.67928156-0.67634962
Kurtosis-1.3707288-1.3558212
Mean-68430.414-68618.029
Median Absolute Deviation (MAD)00
Skewness0.793330430.8027541
Sum-1.8394779 × 109-7.9054831 × 108
Variance2.1607175 × 1092.1538674 × 109
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:30.943309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
-9999918396
68.4%
2711
 
2.6%
3638
 
2.4%
4563
 
2.1%
6448
 
1.7%
5438
 
1.6%
1394
 
1.5%
7353
 
1.3%
8346
 
1.3%
9310
 
1.2%
Other values (27)4284
 
15.9%
ValueCountFrequency (%)
-999997906
68.6%
2301
 
2.6%
3281
 
2.4%
4249
 
2.2%
5223
 
1.9%
6200
 
1.7%
1163
 
1.4%
7159
 
1.4%
8147
 
1.3%
10131
 
1.1%
Other values (27)1761
 
15.3%
ValueCountFrequency (%)
-9999918396
68.4%
090
 
0.3%
1394
 
1.5%
2711
 
2.6%
3638
 
2.4%
4563
 
2.1%
5438
 
1.6%
6448
 
1.7%
7353
 
1.3%
8346
 
1.3%
ValueCountFrequency (%)
-999997906
68.6%
035
 
0.3%
1163
 
1.4%
2301
 
2.6%
3281
 
2.4%
4249
 
2.2%
5223
 
1.9%
6200
 
1.7%
7159
 
1.4%
8147
 
1.3%
ValueCountFrequency (%)
-999997906
29.4%
035
 
0.1%
1163
 
0.6%
2301
 
1.1%
3281
 
1.0%
4249
 
0.9%
5223
 
0.8%
6200
 
0.7%
7159
 
0.6%
8147
 
0.5%
ValueCountFrequency (%)
-9999918396
159.7%
090
 
0.8%
1394
 
3.4%
2711
 
6.2%
3638
 
5.5%
4563
 
4.9%
5438
 
3.8%
6448
 
3.9%
7353
 
3.1%
8346
 
3.0%

num_times_delinquent
Real number (ℝ)

 TrainTest
Distinct5548
Distinct (%)0.2%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.73308281.7081851
 TrainTest
Minimum00
Maximum6574
Zeros183967906
Zeros (%)68.4%68.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile1010
Maximum6574
Range6574
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation4.50624514.5226371
Coefficient of variation (CV)2.60013262.6476271
Kurtosis36.68130444.355703
Mean1.73308281.7081851
Median Absolute Deviation (MAD)00
Skewness4.93783215.3499869
Sum4658719680
Variance20.30624520.454246
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
018396
68.4%
12379
 
8.9%
21371
 
5.1%
3948
 
3.5%
4606
 
2.3%
5499
 
1.9%
6357
 
1.3%
7335
 
1.2%
8287
 
1.1%
9229
 
0.9%
Other values (45)1474
 
5.5%
ValueCountFrequency (%)
07906
68.6%
11010
 
8.8%
2571
 
5.0%
3418
 
3.6%
4294
 
2.6%
5182
 
1.6%
6180
 
1.6%
7125
 
1.1%
8124
 
1.1%
9107
 
0.9%
Other values (38)604
 
5.2%
ValueCountFrequency (%)
018396
68.4%
12379
 
8.9%
21371
 
5.1%
3948
 
3.5%
4606
 
2.3%
5499
 
1.9%
6357
 
1.3%
7335
 
1.2%
8287
 
1.1%
9229
 
0.9%
ValueCountFrequency (%)
07906
68.6%
11010
 
8.8%
2571
 
5.0%
3418
 
3.6%
4294
 
2.6%
5182
 
1.6%
6180
 
1.6%
7125
 
1.1%
8124
 
1.1%
9107
 
0.9%
ValueCountFrequency (%)
07906
29.4%
11010
 
3.8%
2571
 
2.1%
3418
 
1.6%
4294
 
1.1%
5182
 
0.7%
6180
 
0.7%
7125
 
0.5%
8124
 
0.5%
9107
 
0.4%
ValueCountFrequency (%)
018396
159.7%
12379
 
20.6%
21371
 
11.9%
3948
 
8.2%
4606
 
5.3%
5499
 
4.3%
6357
 
3.1%
7335
 
2.9%
8287
 
2.5%
9229
 
2.0%

max_delinquency_level
Real number (ℝ)

 TrainTest
Distinct395293
Distinct (%)1.5%2.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-68410.605-68598.56
 TrainTest
Minimum-99999-99999
Maximum900900
Zeros00
Zeros (%)0.0%0.0%
Negative183967906
Negative (%)68.4%68.6%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:31.040260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q1-99999-99999
median-99999-99999
Q32017
95-th percentile9593
Maximum900900
Range100899100899
Interquartile range (IQR)100019100016

Descriptive statistics

 TrainTest
Standard deviation46512.7446438.622
Coefficient of variation (CV)-0.6799054-0.67696206
Kurtosis-1.3707063-1.3557988
Mean-68410.605-68598.56
Median Absolute Deviation (MAD)00
Skewness0.793337830.80276144
Sum-1.8389455 × 109-7.9032401 × 108
Variance2.163435 × 1092.1565457 × 109
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:31.143781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9999918396
68.4%
3375
 
1.4%
26370
 
1.4%
30369
 
1.4%
28326
 
1.2%
25305
 
1.1%
60257
 
1.0%
27248
 
0.9%
89231
 
0.9%
24221
 
0.8%
Other values (385)5783
 
21.5%
ValueCountFrequency (%)
-999997906
68.6%
26187
 
1.6%
30172
 
1.5%
3170
 
1.5%
28136
 
1.2%
25119
 
1.0%
89112
 
1.0%
60107
 
0.9%
2798
 
0.9%
2989
 
0.8%
Other values (283)2425
 
21.0%
ValueCountFrequency (%)
-9999918396
68.4%
1152
 
0.6%
275
 
0.3%
3375
 
1.4%
469
 
0.3%
595
 
0.4%
656
 
0.2%
795
 
0.4%
862
 
0.2%
986
 
0.3%
ValueCountFrequency (%)
-999997906
68.6%
143
 
0.4%
247
 
0.4%
3170
 
1.5%
428
 
0.2%
548
 
0.4%
632
 
0.3%
736
 
0.3%
827
 
0.2%
948
 
0.4%
ValueCountFrequency (%)
-999997906
29.4%
143
 
0.2%
247
 
0.2%
3170
 
0.6%
428
 
0.1%
548
 
0.2%
632
 
0.1%
736
 
0.1%
827
 
0.1%
948
 
0.2%
ValueCountFrequency (%)
-9999918396
159.7%
1152
 
1.3%
275
 
0.7%
3375
 
3.3%
469
 
0.6%
595
 
0.8%
656
 
0.5%
795
 
0.8%
862
 
0.5%
986
 
0.7%

max_recent_level_of_deliq
Real number (ℝ)

 TrainTest
Distinct270194
Distinct (%)1.0%1.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean14.50641714.580245
 TrainTest
Minimum00
Maximum900900
Zeros183967906
Zeros (%)68.4%68.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:31.242525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q31111
95-th percentile6059
Maximum900900
Range900900
Interquartile range (IQR)1111

Descriptive statistics

 TrainTest
Standard deviation58.00011960.113427
Coefficient of variation (CV)3.99823874.1229368
Kurtosis144.96978145.63771
Mean14.50641714.580245
Median Absolute Deviation (MAD)00
Skewness10.87755810.995845
Sum389947167979
Variance3364.01383613.6241
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:31.337905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018396
68.4%
30632
 
2.4%
25525
 
2.0%
26520
 
1.9%
3489
 
1.8%
28456
 
1.7%
27407
 
1.5%
29345
 
1.3%
24314
 
1.2%
60222
 
0.8%
Other values (260)4575
 
17.0%
ValueCountFrequency (%)
07906
68.6%
30289
 
2.5%
26227
 
2.0%
25221
 
1.9%
3208
 
1.8%
28180
 
1.6%
27168
 
1.5%
29159
 
1.4%
24134
 
1.2%
60116
 
1.0%
Other values (184)1913
 
16.6%
ValueCountFrequency (%)
018396
68.4%
1211
 
0.8%
2107
 
0.4%
3489
 
1.8%
487
 
0.3%
5112
 
0.4%
687
 
0.3%
7155
 
0.6%
8103
 
0.4%
9126
 
0.5%
ValueCountFrequency (%)
07906
68.6%
163
 
0.5%
263
 
0.5%
3208
 
1.8%
436
 
0.3%
557
 
0.5%
644
 
0.4%
762
 
0.5%
846
 
0.4%
967
 
0.6%
ValueCountFrequency (%)
07906
29.4%
163
 
0.2%
263
 
0.2%
3208
 
0.8%
436
 
0.1%
557
 
0.2%
644
 
0.2%
762
 
0.2%
846
 
0.2%
967
 
0.2%
ValueCountFrequency (%)
018396
159.7%
1211
 
1.8%
2107
 
0.9%
3489
 
4.2%
487
 
0.8%
5112
 
1.0%
687
 
0.8%
7155
 
1.3%
8103
 
0.9%
9126
 
1.1%

num_deliq_6mts
Real number (ℝ)

 TrainTest
Distinct1211
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.220713520.22237653
 TrainTest
Minimum00
Maximum1210
Zeros2404710269
Zeros (%)89.5%89.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile22
Maximum1210
Range1210
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.797072090.80062212
Coefficient of variation (CV)3.61134243.6002995
Kurtosis31.18830332.155318
Mean0.220713520.22237653
Median Absolute Deviation (MAD)00
Skewness4.9495495.0578781
Sum59332562
Variance0.635323910.64099578
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
024047
89.5%
11368
 
5.1%
2649
 
2.4%
3354
 
1.3%
4266
 
1.0%
5127
 
0.5%
630
 
0.1%
815
 
0.1%
715
 
0.1%
105
 
< 0.1%
Other values (2)5
 
< 0.1%
ValueCountFrequency (%)
010269
89.1%
1630
 
5.5%
2293
 
2.5%
3151
 
1.3%
481
 
0.7%
554
 
0.5%
624
 
0.2%
89
 
0.1%
75
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
024047
89.5%
11368
 
5.1%
2649
 
2.4%
3354
 
1.3%
4266
 
1.0%
5127
 
0.5%
630
 
0.1%
715
 
0.1%
815
 
0.1%
93
 
< 0.1%
ValueCountFrequency (%)
010269
89.1%
1630
 
5.5%
2293
 
2.5%
3151
 
1.3%
481
 
0.7%
554
 
0.5%
624
 
0.2%
75
 
< 0.1%
89
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
010269
38.2%
1630
 
2.3%
2293
 
1.1%
3151
 
0.6%
481
 
0.3%
554
 
0.2%
624
 
0.1%
75
 
< 0.1%
89
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
024047
208.7%
11368
 
11.9%
2649
 
5.6%
3354
 
3.1%
4266
 
2.3%
5127
 
1.1%
630
 
0.3%
715
 
0.1%
815
 
0.1%
93
 
< 0.1%

num_deliq_12mts
Real number (ℝ)

 TrainTest
Distinct2322
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.54845430.55568093
 TrainTest
Minimum00
Maximum2422
Zeros220829429
Zeros (%)82.1%81.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile44
Maximum2422
Range2422
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation1.66132861.6759256
Coefficient of variation (CV)3.02911033.0159855
Kurtosis28.16266829.842615
Mean0.54845430.55568093
Median Absolute Deviation (MAD)00
Skewness4.61698064.7420555
Sum147436402
Variance2.76001262.8087266
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
022082
82.1%
11830
 
6.8%
2971
 
3.6%
3594
 
2.2%
4361
 
1.3%
5293
 
1.1%
6221
 
0.8%
8136
 
0.5%
7123
 
0.5%
1093
 
0.3%
Other values (13)177
 
0.7%
ValueCountFrequency (%)
09429
81.8%
1768
 
6.7%
2448
 
3.9%
3287
 
2.5%
4160
 
1.4%
5118
 
1.0%
693
 
0.8%
762
 
0.5%
844
 
0.4%
1039
 
0.3%
Other values (12)73
 
0.6%
ValueCountFrequency (%)
022082
82.1%
11830
 
6.8%
2971
 
3.6%
3594
 
2.2%
4361
 
1.3%
5293
 
1.1%
6221
 
0.8%
7123
 
0.5%
8136
 
0.5%
966
 
0.2%
ValueCountFrequency (%)
09429
81.8%
1768
 
6.7%
2448
 
3.9%
3287
 
2.5%
4160
 
1.4%
5118
 
1.0%
693
 
0.8%
762
 
0.5%
844
 
0.4%
917
 
0.1%
ValueCountFrequency (%)
09429
35.1%
1768
 
2.9%
2448
 
1.7%
3287
 
1.1%
4160
 
0.6%
5118
 
0.4%
693
 
0.3%
762
 
0.2%
844
 
0.2%
917
 
0.1%
ValueCountFrequency (%)
022082
191.7%
11830
 
15.9%
2971
 
8.4%
3594
 
5.2%
4361
 
3.1%
5293
 
2.5%
6221
 
1.9%
7123
 
1.1%
8136
 
1.2%
966
 
0.6%

num_deliq_6_12mts
Real number (ℝ)

 TrainTest
Distinct1515
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.327740780.3333044
 TrainTest
Minimum00
Maximum1717
Zeros234039974
Zeros (%)87.1%86.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:31.408607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile22
Maximum1717
Range1717
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation1.09195181.092752
Coefficient of variation (CV)3.33175453.2785405
Kurtosis28.01684328.862211
Mean0.327740780.3333044
Median Absolute Deviation (MAD)00
Skewness4.6526164.677795
Sum88103840
Variance1.19235881.1941069
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:31.466783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
023403
87.1%
11401
 
5.2%
2793
 
3.0%
3449
 
1.7%
4317
 
1.2%
6223
 
0.8%
5185
 
0.7%
748
 
0.2%
823
 
0.1%
910
 
< 0.1%
Other values (5)29
 
0.1%
ValueCountFrequency (%)
09974
86.6%
1636
 
5.5%
2353
 
3.1%
3212
 
1.8%
4138
 
1.2%
679
 
0.7%
575
 
0.7%
722
 
0.2%
811
 
0.1%
911
 
0.1%
Other values (5)10
 
0.1%
ValueCountFrequency (%)
023403
87.1%
11401
 
5.2%
2793
 
3.0%
3449
 
1.7%
4317
 
1.2%
5185
 
0.7%
6223
 
0.8%
748
 
0.2%
823
 
0.1%
910
 
< 0.1%
ValueCountFrequency (%)
09974
86.6%
1636
 
5.5%
2353
 
3.1%
3212
 
1.8%
4138
 
1.2%
575
 
0.7%
679
 
0.7%
722
 
0.2%
811
 
0.1%
911
 
0.1%
ValueCountFrequency (%)
09974
37.1%
1636
 
2.4%
2353
 
1.3%
3212
 
0.8%
4138
 
0.5%
575
 
0.3%
679
 
0.3%
722
 
0.1%
811
 
< 0.1%
911
 
< 0.1%
ValueCountFrequency (%)
023403
203.1%
11401
 
12.2%
2793
 
6.9%
3449
 
3.9%
4317
 
2.8%
5185
 
1.6%
6223
 
1.9%
748
 
0.4%
823
 
0.2%
910
 
0.1%

max_deliq_6mts
Real number (ℝ)

 TrainTest
Distinct157126
Distinct (%)0.6%1.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-22780.972-22875.031
 TrainTest
Minimum-99999-99999
Maximum900900
Zeros179227633
Zeros (%)66.7%66.3%
Negative61252636
Negative (%)22.8%22.9%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:31.556129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q300
95-th percentile2626
Maximum900900
Range100899100899
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation41947.69742009.969
Coefficient of variation (CV)-1.841348-1.8364989
Kurtosis-0.31600754-0.33230885
Mean-22780.972-22875.031
Median Absolute Deviation (MAD)00
Skewness-1.2976951-1.2914119
Sum-6.123753 × 108-2.6354323 × 108
Variance1.7596093 × 1091.7648375 × 109
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:31.669299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017922
66.7%
-999996125
 
22.8%
3329
 
1.2%
28179
 
0.7%
26169
 
0.6%
27138
 
0.5%
25132
 
0.5%
24110
 
0.4%
10102
 
0.4%
3099
 
0.4%
Other values (147)1576
 
5.9%
ValueCountFrequency (%)
07633
66.3%
-999992636
 
22.9%
3148
 
1.3%
2570
 
0.6%
2869
 
0.6%
2669
 
0.6%
3065
 
0.6%
2453
 
0.5%
2752
 
0.5%
2942
 
0.4%
Other values (116)684
 
5.9%
ValueCountFrequency (%)
-999996125
 
22.8%
017922
66.7%
168
 
0.3%
232
 
0.1%
3329
 
1.2%
433
 
0.1%
533
 
0.1%
629
 
0.1%
733
 
0.1%
839
 
0.1%
ValueCountFrequency (%)
-999992636
 
22.9%
07633
66.3%
124
 
0.2%
226
 
0.2%
3148
 
1.3%
47
 
0.1%
518
 
0.2%
613
 
0.1%
711
 
0.1%
820
 
0.2%
ValueCountFrequency (%)
-999992636
 
9.8%
07633
28.4%
124
 
0.1%
226
 
0.1%
3148
 
0.6%
47
 
< 0.1%
518
 
0.1%
613
 
< 0.1%
711
 
< 0.1%
820
 
0.1%
ValueCountFrequency (%)
-999996125
 
53.2%
017922
155.6%
168
 
0.6%
232
 
0.3%
3329
 
2.9%
433
 
0.3%
533
 
0.3%
629
 
0.3%
733
 
0.3%
839
 
0.3%

max_deliq_12mts
Real number (ℝ)

 TrainTest
Distinct220171
Distinct (%)0.8%1.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-19038.514-19329.928
 TrainTest
Minimum-99999-99999
Maximum900900
Zeros169627201
Zeros (%)63.1%62.5%
Negative51202228
Negative (%)19.0%19.3%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:31.782463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q300
95-th percentile4549
Maximum900900
Range100899100899
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation39271.45339500.79
Coefficient of variation (CV)-2.0627374-2.0435043
Kurtosis0.485786290.41144904
Mean-19038.514-19329.928
Median Absolute Deviation (MAD)00
Skewness-1.576624-1.5528588
Sum-5.1177428 × 108-2.227001 × 108
Variance1.542247 × 1091.5603124 × 109
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:31.887853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016962
63.1%
-999995120
 
19.0%
3381
 
1.4%
28288
 
1.1%
26263
 
1.0%
27240
 
0.9%
25210
 
0.8%
30188
 
0.7%
29162
 
0.6%
24157
 
0.6%
Other values (210)2910
 
10.8%
ValueCountFrequency (%)
07201
62.5%
-999992228
 
19.3%
3164
 
1.4%
26124
 
1.1%
28121
 
1.1%
2599
 
0.9%
3089
 
0.8%
2782
 
0.7%
2980
 
0.7%
2476
 
0.7%
Other values (161)1257
 
10.9%
ValueCountFrequency (%)
-999995120
 
19.0%
016962
63.1%
1102
 
0.4%
256
 
0.2%
3381
 
1.4%
445
 
0.2%
563
 
0.2%
646
 
0.2%
786
 
0.3%
858
 
0.2%
ValueCountFrequency (%)
-999992228
 
19.3%
07201
62.5%
131
 
0.3%
243
 
0.4%
3164
 
1.4%
418
 
0.2%
538
 
0.3%
627
 
0.2%
722
 
0.2%
821
 
0.2%
ValueCountFrequency (%)
-999992228
 
8.3%
07201
26.8%
131
 
0.1%
243
 
0.2%
3164
 
0.6%
418
 
0.1%
538
 
0.1%
627
 
0.1%
722
 
0.1%
821
 
0.1%
ValueCountFrequency (%)
-999995120
 
44.4%
016962
147.2%
1102
 
0.9%
256
 
0.5%
3381
 
3.3%
445
 
0.4%
563
 
0.5%
646
 
0.4%
786
 
0.7%
858
 
0.5%

num_times_30p_dpd
Real number (ℝ)

 TrainTest
Distinct4439
Distinct (%)0.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.791376810.79177155
 TrainTest
Minimum00
Maximum6059
Zeros224969667
Zeros (%)83.7%83.9%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile55
Maximum6059
Range6059
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation2.99574383.0448821
Coefficient of variation (CV)3.78548343.8456574
Kurtosis76.29862982.653981
Mean0.791376810.79177155
Median Absolute Deviation (MAD)00
Skewness7.1737927.4978316
Sum212739122
Variance8.97448079.2713073
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
022496
83.7%
11326
 
4.9%
2840
 
3.1%
3485
 
1.8%
4291
 
1.1%
5248
 
0.9%
6173
 
0.6%
7151
 
0.6%
8132
 
0.5%
10109
 
0.4%
Other values (34)630
 
2.3%
ValueCountFrequency (%)
09667
83.9%
1545
 
4.7%
2361
 
3.1%
3212
 
1.8%
4113
 
1.0%
598
 
0.9%
681
 
0.7%
879
 
0.7%
759
 
0.5%
1044
 
0.4%
Other values (29)262
 
2.3%
ValueCountFrequency (%)
022496
83.7%
11326
 
4.9%
2840
 
3.1%
3485
 
1.8%
4291
 
1.1%
5248
 
0.9%
6173
 
0.6%
7151
 
0.6%
8132
 
0.5%
9104
 
0.4%
ValueCountFrequency (%)
09667
83.9%
1545
 
4.7%
2361
 
3.1%
3212
 
1.8%
4113
 
1.0%
598
 
0.9%
681
 
0.7%
759
 
0.5%
879
 
0.7%
938
 
0.3%
ValueCountFrequency (%)
09667
36.0%
1545
 
2.0%
2361
 
1.3%
3212
 
0.8%
4113
 
0.4%
598
 
0.4%
681
 
0.3%
759
 
0.2%
879
 
0.3%
938
 
0.1%
ValueCountFrequency (%)
022496
195.3%
11326
 
11.5%
2840
 
7.3%
3485
 
4.2%
4291
 
2.5%
5248
 
2.2%
6173
 
1.5%
7151
 
1.3%
8132
 
1.1%
9104
 
0.9%

num_times_60p_dpd
Real number (ℝ)

 TrainTest
Distinct4136
Distinct (%)0.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.472080650.47903828
 TrainTest
Minimum00
Maximum4952
Zeros2418510396
Zeros (%)90.0%90.2%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile22
Maximum4952
Range4952
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation2.35631192.4479156
Coefficient of variation (CV)4.99133345.1100625
Kurtosis110.27747114.69481
Mean0.472080650.47903828
Median Absolute Deviation (MAD)00
Skewness8.9975919.2481878
Sum126905519
Variance5.5522065.9922906
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
024185
90.0%
1942
 
3.5%
2461
 
1.7%
3241
 
0.9%
4204
 
0.8%
5150
 
0.6%
6104
 
0.4%
787
 
0.3%
884
 
0.3%
959
 
0.2%
Other values (31)364
 
1.4%
ValueCountFrequency (%)
010396
90.2%
1380
 
3.3%
2195
 
1.7%
3101
 
0.9%
480
 
0.7%
557
 
0.5%
653
 
0.5%
742
 
0.4%
829
 
0.3%
1025
 
0.2%
Other values (26)163
 
1.4%
ValueCountFrequency (%)
024185
90.0%
1942
 
3.5%
2461
 
1.7%
3241
 
0.9%
4204
 
0.8%
5150
 
0.6%
6104
 
0.4%
787
 
0.3%
884
 
0.3%
959
 
0.2%
ValueCountFrequency (%)
010396
90.2%
1380
 
3.3%
2195
 
1.7%
3101
 
0.9%
480
 
0.7%
557
 
0.5%
653
 
0.5%
742
 
0.4%
829
 
0.3%
924
 
0.2%
ValueCountFrequency (%)
010396
38.7%
1380
 
1.4%
2195
 
0.7%
3101
 
0.4%
480
 
0.3%
557
 
0.2%
653
 
0.2%
742
 
0.2%
829
 
0.1%
924
 
0.1%
ValueCountFrequency (%)
024185
209.9%
1942
 
8.2%
2461
 
4.0%
3241
 
2.1%
4204
 
1.8%
5150
 
1.3%
6104
 
0.9%
787
 
0.8%
884
 
0.7%
959
 
0.5%

num_std
Real number (ℝ)

 TrainTest
Distinct194174
Distinct (%)0.7%1.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean9.2317259.5550733
 TrainTest
Minimum00
Maximum331422
Zeros165777061
Zeros (%)61.7%61.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:31.994319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q399
95-th percentile4850
Maximum331422
Range331422
Interquartile range (IQR)99

Descriptive statistics

 TrainTest
Standard deviation21.19424222.120938
Coefficient of variation (CV)2.29580522.3150987
Kurtosis30.19071337.521028
Mean9.2317259.5550733
Median Absolute Deviation (MAD)00
Skewness4.41103044.7226004
Sum248158110084
Variance449.19589489.33588
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:32.092528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016577
61.7%
1772
 
2.9%
2530
 
2.0%
3518
 
1.9%
4392
 
1.5%
6352
 
1.3%
7314
 
1.2%
5311
 
1.2%
11294
 
1.1%
8287
 
1.1%
Other values (184)6534
 
24.3%
ValueCountFrequency (%)
07061
61.3%
1341
 
3.0%
2230
 
2.0%
3229
 
2.0%
4168
 
1.5%
8146
 
1.3%
6144
 
1.2%
5138
 
1.2%
9130
 
1.1%
7126
 
1.1%
Other values (164)2808
 
24.4%
ValueCountFrequency (%)
016577
61.7%
1772
 
2.9%
2530
 
2.0%
3518
 
1.9%
4392
 
1.5%
5311
 
1.2%
6352
 
1.3%
7314
 
1.2%
8287
 
1.1%
9255
 
0.9%
ValueCountFrequency (%)
07061
61.3%
1341
 
3.0%
2230
 
2.0%
3229
 
2.0%
4168
 
1.5%
5138
 
1.2%
6144
 
1.2%
7126
 
1.1%
8146
 
1.3%
9130
 
1.1%
ValueCountFrequency (%)
07061
26.3%
1341
 
1.3%
2230
 
0.9%
3229
 
0.9%
4168
 
0.6%
5138
 
0.5%
6144
 
0.5%
7126
 
0.5%
8146
 
0.5%
9130
 
0.5%
ValueCountFrequency (%)
016577
143.9%
1772
 
6.7%
2530
 
4.6%
3518
 
4.5%
4392
 
3.4%
5311
 
2.7%
6352
 
3.1%
7314
 
2.7%
8287
 
2.5%
9255
 
2.2%

num_std_6mts
Real number (ℝ)

 TrainTest
Distinct4639
Distinct (%)0.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.48766791.5252148
 TrainTest
Minimum00
Maximum6060
Zeros195298393
Zeros (%)72.6%72.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile88
Maximum6060
Range6060
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation3.3645573.4703722
Coefficient of variation (CV)2.26163192.2753334
Kurtosis32.24698828.439431
Mean1.48766791.5252148
Median Absolute Deviation (MAD)00
Skewness4.25419284.1024165
Sum3999017572
Variance11.32024412.043483
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
019529
72.6%
41963
 
7.3%
51350
 
5.0%
1718
 
2.7%
3681
 
2.5%
2649
 
2.4%
8370
 
1.4%
10326
 
1.2%
6248
 
0.9%
7219
 
0.8%
Other values (36)828
 
3.1%
ValueCountFrequency (%)
08393
72.8%
4763
 
6.6%
5586
 
5.1%
3304
 
2.6%
1304
 
2.6%
2258
 
2.2%
8169
 
1.5%
10136
 
1.2%
6131
 
1.1%
794
 
0.8%
Other values (29)383
 
3.3%
ValueCountFrequency (%)
019529
72.6%
1718
 
2.7%
2649
 
2.4%
3681
 
2.5%
41963
 
7.3%
51350
 
5.0%
6248
 
0.9%
7219
 
0.8%
8370
 
1.4%
9189
 
0.7%
ValueCountFrequency (%)
08393
72.8%
1304
 
2.6%
2258
 
2.2%
3304
 
2.6%
4763
 
6.6%
5586
 
5.1%
6131
 
1.1%
794
 
0.8%
8169
 
1.5%
963
 
0.5%
ValueCountFrequency (%)
08393
31.2%
1304
 
1.1%
2258
 
1.0%
3304
 
1.1%
4763
 
2.8%
5586
 
2.2%
6131
 
0.5%
794
 
0.3%
8169
 
0.6%
963
 
0.2%
ValueCountFrequency (%)
019529
169.5%
1718
 
6.2%
2649
 
5.6%
3681
 
5.9%
41963
 
17.0%
51350
 
11.7%
6248
 
2.2%
7219
 
1.9%
8370
 
3.2%
9189
 
1.6%

num_std_12mts
Real number (ℝ)

 TrainTest
Distinct8277
Distinct (%)0.3%0.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean3.3351813.4004861
 TrainTest
Minimum00
Maximum122107
Zeros186587999
Zeros (%)69.4%69.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:32.191329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q333
95-th percentile1818
Maximum122107
Range122107
Interquartile range (IQR)33

Descriptive statistics

 TrainTest
Standard deviation7.52176917.7534163
Coefficient of variation (CV)2.25528062.2800906
Kurtosis28.62194528.022455
Mean3.3351813.4004861
Median Absolute Deviation (MAD)00
Skewness4.14907684.2133651
Sum8965339177
Variance56.5770160.115465
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:32.292202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018658
69.4%
101105
 
4.1%
11786
 
2.9%
1704
 
2.6%
2540
 
2.0%
8441
 
1.6%
3432
 
1.6%
4430
 
1.6%
9428
 
1.6%
7407
 
1.5%
Other values (72)2950
 
11.0%
ValueCountFrequency (%)
07999
69.4%
10434
 
3.8%
11350
 
3.0%
1284
 
2.5%
2220
 
1.9%
9199
 
1.7%
4199
 
1.7%
8181
 
1.6%
3179
 
1.6%
5177
 
1.5%
Other values (67)1299
 
11.3%
ValueCountFrequency (%)
018658
69.4%
1704
 
2.6%
2540
 
2.0%
3432
 
1.6%
4430
 
1.6%
5344
 
1.3%
6380
 
1.4%
7407
 
1.5%
8441
 
1.6%
9428
 
1.6%
ValueCountFrequency (%)
07999
69.4%
1284
 
2.5%
2220
 
1.9%
3179
 
1.6%
4199
 
1.7%
5177
 
1.5%
6164
 
1.4%
7165
 
1.4%
8181
 
1.6%
9199
 
1.7%
ValueCountFrequency (%)
07999
29.8%
1284
 
1.1%
2220
 
0.8%
3179
 
0.7%
4199
 
0.7%
5177
 
0.7%
6164
 
0.6%
7165
 
0.6%
8181
 
0.7%
9199
 
0.7%
ValueCountFrequency (%)
018658
161.9%
1704
 
6.1%
2540
 
4.7%
3432
 
3.7%
4430
 
3.7%
5344
 
3.0%
6380
 
3.3%
7407
 
3.5%
8441
 
3.8%
9428
 
3.7%

num_sub
Real number (ℝ)

 TrainTest
Distinct2219
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0609724340.054509157
 TrainTest
Minimum00
Maximum4022
Zeros2656211384
Zeros (%)98.8%98.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum4022
Range4022
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.789087270.70844167
Coefficient of variation (CV)12.94170512.996746
Kurtosis728.60309436.97611
Mean0.0609724340.054509157
Median Absolute Deviation (MAD)00
Skewness22.11940819.007643
Sum1639628
Variance0.622658710.5018896
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
026562
98.8%
183
 
0.3%
245
 
0.2%
636
 
0.1%
332
 
0.1%
419
 
0.1%
718
 
0.1%
1016
 
0.1%
515
 
0.1%
813
 
< 0.1%
Other values (12)42
 
0.2%
ValueCountFrequency (%)
011384
98.8%
143
 
0.4%
219
 
0.2%
318
 
0.2%
512
 
0.1%
107
 
0.1%
47
 
0.1%
86
 
0.1%
76
 
0.1%
65
 
< 0.1%
Other values (9)14
 
0.1%
ValueCountFrequency (%)
026562
98.8%
183
 
0.3%
245
 
0.2%
332
 
0.1%
419
 
0.1%
515
 
0.1%
636
 
0.1%
718
 
0.1%
813
 
< 0.1%
98
 
< 0.1%
ValueCountFrequency (%)
011384
98.8%
143
 
0.4%
219
 
0.2%
318
 
0.2%
47
 
0.1%
512
 
0.1%
65
 
< 0.1%
76
 
0.1%
86
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
011384
42.3%
143
 
0.2%
219
 
0.1%
318
 
0.1%
47
 
< 0.1%
512
 
< 0.1%
65
 
< 0.1%
76
 
< 0.1%
86
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
026562
230.6%
183
 
0.7%
245
 
0.4%
332
 
0.3%
419
 
0.2%
515
 
0.1%
636
 
0.3%
718
 
0.2%
813
 
0.1%
98
 
0.1%

num_sub_6mts
Numeric

 TrainTest
Distinct65
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
26856 
1
 
12
2
 
6
3
 
4
4
 
2
0
11514 
1
 
3
5
 
2
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11521
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique12 ?
Unique (%)< 0.1%< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026856
99.9%
112
 
< 0.1%
26
 
< 0.1%
34
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
011514
99.9%
13
 
< 0.1%
52
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%

Length

2026-01-17T22:57:32.372051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:32.418701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:32.458317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
011514
99.9%
13
 
< 0.1%
52
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011514
99.9%
13
 
< 0.1%
52
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
011514
99.9%
13
 
< 0.1%
52
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
011514
99.9%
13
 
< 0.1%
52
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
011514
99.9%
13
 
< 0.1%
52
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%

num_sub_12mts
Real number (ℝ)

 TrainTest
Distinct1210
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.00900264130.008245812
 TrainTest
Minimum00
Maximum1211
Zeros2680011489
Zeros (%)99.7%99.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum1211
Range1211
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.211272840.2129115
Coefficient of variation (CV)23.46787225.820562
Kurtosis1256.20491507.6331
Mean0.00900264130.008245812
Median Absolute Deviation (MAD)00
Skewness32.33341136.144618
Sum24295
Variance0.0446362110.045331306
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
026800
99.7%
127
 
0.1%
225
 
0.1%
612
 
< 0.1%
35
 
< 0.1%
44
 
< 0.1%
53
 
< 0.1%
111
 
< 0.1%
81
 
< 0.1%
121
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
011489
99.7%
114
 
0.1%
26
 
0.1%
33
 
< 0.1%
43
 
< 0.1%
82
 
< 0.1%
51
 
< 0.1%
101
 
< 0.1%
61
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
026800
99.7%
127
 
0.1%
225
 
0.1%
35
 
< 0.1%
44
 
< 0.1%
53
 
< 0.1%
612
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
011489
99.7%
114
 
0.1%
26
 
0.1%
33
 
< 0.1%
43
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
82
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
011489
42.7%
114
 
0.1%
26
 
< 0.1%
33
 
< 0.1%
43
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
82
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
026800
232.6%
127
 
0.2%
225
 
0.2%
35
 
< 0.1%
44
 
< 0.1%
53
 
< 0.1%
612
 
0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%

num_dbt
Real number (ℝ)

 TrainTest
Distinct2317
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.023139020.020657929
 TrainTest
Minimum00
Maximum3226
Zeros2679011489
Zeros (%)99.7%99.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum3226
Range3226
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.581958650.58771298
Coefficient of variation (CV)25.15053128.449753
Kurtosis1356.76121445.1804
Mean0.023139020.020657929
Median Absolute Deviation (MAD)00
Skewness34.12793436.478921
Sum622238
Variance0.338675880.34540655
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
026790
99.7%
132
 
0.1%
58
 
< 0.1%
28
 
< 0.1%
66
 
< 0.1%
135
 
< 0.1%
95
 
< 0.1%
34
 
< 0.1%
113
 
< 0.1%
122
 
< 0.1%
Other values (13)18
 
0.1%
ValueCountFrequency (%)
011489
99.7%
110
 
0.1%
24
 
< 0.1%
33
 
< 0.1%
92
 
< 0.1%
262
 
< 0.1%
241
 
< 0.1%
61
 
< 0.1%
141
 
< 0.1%
221
 
< 0.1%
Other values (7)7
 
0.1%
ValueCountFrequency (%)
026790
99.7%
132
 
0.1%
28
 
< 0.1%
34
 
< 0.1%
58
 
< 0.1%
66
 
< 0.1%
72
 
< 0.1%
82
 
< 0.1%
95
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
011489
99.7%
110
 
0.1%
24
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
011489
42.7%
110
 
< 0.1%
24
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
026790
232.5%
132
 
0.3%
28
 
0.1%
34
 
< 0.1%
58
 
0.1%
66
 
0.1%
72
 
< 0.1%
82
 
< 0.1%
95
 
< 0.1%
101
 
< 0.1%

num_dbt_6mts
Categorical

 TrainTest
Distinct45
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
26873 
4
 
4
5
 
2
3
 
2
0
11517 
5
 
1
3
 
1
2
 
1
4
 
1

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters45
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique04 ?
Unique (%)0.0%< 0.1%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
026873
> 99.9%
44
 
< 0.1%
52
 
< 0.1%
32
 
< 0.1%
ValueCountFrequency (%)
011517
> 99.9%
51
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Length

2026-01-17T22:57:32.515534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:32.559104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:32.867090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
026873
> 99.9%
44
 
< 0.1%
52
 
< 0.1%
32
 
< 0.1%
ValueCountFrequency (%)
011517
> 99.9%
51
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
026873
> 99.9%
44
 
< 0.1%
52
 
< 0.1%
32
 
< 0.1%
ValueCountFrequency (%)
011517
> 99.9%
51
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
026873
> 99.9%
44
 
< 0.1%
52
 
< 0.1%
32
 
< 0.1%
ValueCountFrequency (%)
011517
> 99.9%
51
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
026873
> 99.9%
44
 
< 0.1%
52
 
< 0.1%
32
 
< 0.1%
ValueCountFrequency (%)
011517
> 99.9%
51
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
026873
> 99.9%
44
 
< 0.1%
52
 
< 0.1%
32
 
< 0.1%
ValueCountFrequency (%)
011517
> 99.9%
51
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

num_dbt_12mts
Real number (ℝ)

 TrainTest
Distinct98
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.00342249170.0044266991
 TrainTest
Minimum00
Maximum1111
Zeros2686411512
Zeros (%)99.9%99.9%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum1111
Range1111
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.167448150.18791025
Coefficient of variation (CV)48.925842.449295
Kurtosis3305.29252489.1742
Mean0.00342249170.0044266991
Median Absolute Deviation (MAD)00
Skewness56.20134448.565995
Sum9251
Variance0.0280388810.035310264
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
026864
99.9%
24
 
< 0.1%
13
 
< 0.1%
93
 
< 0.1%
112
 
< 0.1%
102
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
ValueCountFrequency (%)
011512
99.9%
42
 
< 0.1%
22
 
< 0.1%
111
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%
11
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
026864
99.9%
13
 
< 0.1%
24
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
93
 
< 0.1%
102
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
011512
99.9%
11
 
< 0.1%
22
 
< 0.1%
42
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
011512
42.8%
11
 
< 0.1%
22
 
< 0.1%
42
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
026864
233.2%
13
 
< 0.1%
24
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
93
 
< 0.1%
102
 
< 0.1%
112
 
< 0.1%

num_lss
Real number (ℝ)

 TrainTest
Distinct1910
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0213905730.0075514278
 TrainTest
Minimum00
Maximum7214
Zeros2682311501
Zeros (%)99.8%99.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum7214
Range7214
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.779210440.25213378
Coefficient of variation (CV)36.42774933.388888
Kurtosis5747.01511974.8213
Mean0.0213905730.0075514278
Median Absolute Deviation (MAD)00
Skewness68.32179642.501132
Sum57587
Variance0.607168910.063571443
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
026823
99.8%
19
 
< 0.1%
37
 
< 0.1%
86
 
< 0.1%
95
 
< 0.1%
45
 
< 0.1%
75
 
< 0.1%
114
 
< 0.1%
23
 
< 0.1%
342
 
< 0.1%
Other values (9)12
 
< 0.1%
ValueCountFrequency (%)
011501
99.8%
18
 
0.1%
33
 
< 0.1%
82
 
< 0.1%
22
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
101
 
< 0.1%
41
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
026823
99.8%
19
 
< 0.1%
23
 
< 0.1%
37
 
< 0.1%
45
 
< 0.1%
61
 
< 0.1%
75
 
< 0.1%
86
 
< 0.1%
95
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
011501
99.8%
18
 
0.1%
22
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
011501
42.8%
18
 
< 0.1%
22
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
026823
232.8%
19
 
0.1%
23
 
< 0.1%
37
 
0.1%
45
 
< 0.1%
61
 
< 0.1%
75
 
< 0.1%
86
 
0.1%
95
 
< 0.1%
102
 
< 0.1%

num_lss_6mts
Categorical

 TrainTest
Distinct54
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
26875 
4
 
2
12
 
2
5
 
1
2
 
1
0
11518 
4
 
1
3
 
1
5
 
1

Length

 TrainTest
Max length21
Median length11
Mean length1.00007441
Min length11

Characters and Unicode

 TrainTest
Total characters2688311521
Distinct characters54
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique23 ?
Unique (%)< 0.1%< 0.1%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
026875
> 99.9%
42
 
< 0.1%
122
 
< 0.1%
51
 
< 0.1%
21
 
< 0.1%
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%

Length

Common Values (Plot)

Train

2026-01-17T22:57:32.908733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:32.943824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
026875
> 99.9%
42
 
< 0.1%
122
 
< 0.1%
51
 
< 0.1%
21
 
< 0.1%
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
026875
> 99.9%
23
 
< 0.1%
42
 
< 0.1%
12
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)26883
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
026875
> 99.9%
23
 
< 0.1%
42
 
< 0.1%
12
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26883
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
026875
> 99.9%
23
 
< 0.1%
42
 
< 0.1%
12
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26883
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
026875
> 99.9%
23
 
< 0.1%
42
 
< 0.1%
12
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%
 TrainTest
Distinct84
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
26868 
1
 
4
10
 
3
30
 
2
5
 
1
Other values (3)
 
3
0
11518 
4
 
1
3
 
1
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11521
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique43 ?
Unique (%)< 0.1%< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026868
> 99.9%
14
 
< 0.1%
103
 
< 0.1%
302
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%
81
 
< 0.1%
21
 
< 0.1%
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
81
 
< 0.1%

Length

2026-01-17T22:57:32.997027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:33.049188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:33.092845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
81
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
81
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
011518
> 99.9%
41
 
< 0.1%
31
 
< 0.1%
81
 
< 0.1%

recent_level_of_deliq
Real number (ℝ)

 TrainTest
Distinct239167
Distinct (%)0.9%1.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean11.94743512.046263
 TrainTest
Minimum00
Maximum900900
Zeros183967906
Zeros (%)68.4%68.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:33.166129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q388
95-th percentile4749
Maximum900900
Range900900
Interquartile range (IQR)88

Descriptive statistics

 TrainTest
Standard deviation50.99981252.557986
Coefficient of variation (CV)4.2686834.3630115
Kurtosis196.32812195.79338
Mean11.94743512.046263
Median Absolute Deviation (MAD)00
Skewness12.68526212.743776
Sum321159138785
Variance2600.98082762.3419
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:33.262878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018396
68.4%
3692
 
2.6%
30574
 
2.1%
25538
 
2.0%
26505
 
1.9%
28414
 
1.5%
27384
 
1.4%
24342
 
1.3%
29311
 
1.2%
1263
 
1.0%
Other values (229)4462
 
16.6%
ValueCountFrequency (%)
07906
68.6%
3295
 
2.6%
30266
 
2.3%
25224
 
1.9%
26209
 
1.8%
28170
 
1.5%
27161
 
1.4%
29159
 
1.4%
24136
 
1.2%
10103
 
0.9%
Other values (157)1892
 
16.4%
ValueCountFrequency (%)
018396
68.4%
1263
 
1.0%
2155
 
0.6%
3692
 
2.6%
4107
 
0.4%
5149
 
0.6%
6125
 
0.5%
7187
 
0.7%
8141
 
0.5%
9147
 
0.5%
ValueCountFrequency (%)
07906
68.6%
182
 
0.7%
294
 
0.8%
3295
 
2.6%
440
 
0.3%
571
 
0.6%
652
 
0.5%
771
 
0.6%
857
 
0.5%
985
 
0.7%
ValueCountFrequency (%)
07906
29.4%
182
 
0.3%
294
 
0.3%
3295
 
1.1%
440
 
0.1%
571
 
0.3%
652
 
0.2%
771
 
0.3%
857
 
0.2%
985
 
0.3%
ValueCountFrequency (%)
018396
159.7%
1263
 
2.3%
2155
 
1.3%
3692
 
6.0%
4107
 
0.9%
5149
 
1.3%
6125
 
1.1%
7187
 
1.6%
8141
 
1.2%
9147
 
1.3%

tot_enq
Real number (ℝ)

 TrainTest
Distinct7768
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11102.546-10644.627
 TrainTest
Minimum-99999-99999
Maximum17694
Zeros00
Zeros (%)0.0%0.0%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:33.361526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q111
median33
Q377
95-th percentile1918
Maximum17694
Range100175100093
Interquartile range (IQR)66

Descriptive statistics

 TrainTest
Standard deviation31425.62430850.689
Coefficient of variation (CV)-2.830488-2.8982404
Kurtosis4.1282984.5112414
Mean-11102.546-10644.627
Median Absolute Deviation (MAD)22
Skewness-2.4754778-2.5515599
Sum-2.9844755 × 108-1.2263675 × 108
Variance9.8756984 × 1089.5176501 × 108
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:33.462257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14555
16.9%
23834
14.3%
33015
11.2%
-999992986
11.1%
42360
8.8%
51741
 
6.5%
61378
 
5.1%
71028
 
3.8%
8815
 
3.0%
9765
 
2.8%
Other values (67)4404
16.4%
ValueCountFrequency (%)
12043
17.7%
21602
13.9%
31319
11.4%
-999991227
10.7%
4992
8.6%
5786
 
6.8%
6625
 
5.4%
7421
 
3.7%
8383
 
3.3%
9299
 
2.6%
Other values (58)1824
15.8%
ValueCountFrequency (%)
-999992986
11.1%
14555
16.9%
23834
14.3%
33015
11.2%
42360
8.8%
51741
 
6.5%
61378
 
5.1%
71028
 
3.8%
8815
 
3.0%
9765
 
2.8%
ValueCountFrequency (%)
-999991227
10.7%
12043
17.7%
21602
13.9%
31319
11.4%
4992
8.6%
5786
 
6.8%
6625
 
5.4%
7421
 
3.7%
8383
 
3.3%
9299
 
2.6%
ValueCountFrequency (%)
-999991227
4.6%
12043
7.6%
21602
6.0%
31319
4.9%
4992
3.7%
5786
 
2.9%
6625
 
2.3%
7421
 
1.6%
8383
 
1.4%
9299
 
1.1%
ValueCountFrequency (%)
-999992986
25.9%
14555
39.5%
23834
33.3%
33015
26.2%
42360
20.5%
51741
 
15.1%
61378
 
12.0%
71028
 
8.9%
8815
 
7.1%
9765
 
6.6%

CC_enq
Real number (ℝ)

 TrainTest
Distinct3327
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11107.356-10649.281
 TrainTest
Minimum-99999-99999
Maximum3935
Zeros175587590
Zeros (%)65.3%65.9%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q300
95-th percentile44
Maximum3935
Range100038100034
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation31423.92330849.082
Coefficient of variation (CV)-2.8291092-2.896823
Kurtosis4.12829864.511242
Mean-11107.356-10649.281
Median Absolute Deviation (MAD)00
Skewness-2.475478-2.5515601
Sum-2.9857684 × 108-1.2269036 × 108
Variance9.8746293 × 1089.5166584 × 108
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
017558
65.3%
-999992986
 
11.1%
12798
 
10.4%
21210
 
4.5%
3647
 
2.4%
4410
 
1.5%
5268
 
1.0%
6219
 
0.8%
8146
 
0.5%
7143
 
0.5%
Other values (23)496
 
1.8%
ValueCountFrequency (%)
07590
65.9%
-999991227
 
10.7%
11199
 
10.4%
2504
 
4.4%
3290
 
2.5%
4181
 
1.6%
5123
 
1.1%
6100
 
0.9%
765
 
0.6%
847
 
0.4%
Other values (17)195
 
1.7%
ValueCountFrequency (%)
-999992986
 
11.1%
017558
65.3%
12798
 
10.4%
21210
 
4.5%
3647
 
2.4%
4410
 
1.5%
5268
 
1.0%
6219
 
0.8%
7143
 
0.5%
8146
 
0.5%
ValueCountFrequency (%)
-999991227
 
10.7%
07590
65.9%
11199
 
10.4%
2504
 
4.4%
3290
 
2.5%
4181
 
1.6%
5123
 
1.1%
6100
 
0.9%
765
 
0.6%
847
 
0.4%
ValueCountFrequency (%)
-999991227
 
4.6%
07590
28.2%
11199
 
4.5%
2504
 
1.9%
3290
 
1.1%
4181
 
0.7%
5123
 
0.5%
6100
 
0.4%
765
 
0.2%
847
 
0.2%
ValueCountFrequency (%)
-999992986
 
25.9%
017558
152.4%
12798
 
24.3%
21210
 
10.5%
3647
 
5.6%
4410
 
3.6%
5268
 
2.3%
6219
 
1.9%
7143
 
1.2%
8146
 
1.3%

CC_enq_L6m
Real number (ℝ)

 TrainTest
Distinct1515
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11107.874-10649.787
 TrainTest
Minimum-99999-99999
Maximum1713
Zeros207038914
Zeros (%)77.0%77.4%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:33.538115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q300
95-th percentile22
Maximum1713
Range100016100012
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation31423.7430848.907
Coefficient of variation (CV)-2.8289608-2.896669
Kurtosis4.12829874.511242
Mean-11107.874-10649.787
Median Absolute Deviation (MAD)00
Skewness-2.475478-2.5515601
Sum-2.9859076 × 108-1.2269619 × 108
Variance9.8745143 × 1089.5165507 × 108
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:33.600791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
020703
77.0%
-999992986
 
11.1%
11822
 
6.8%
2666
 
2.5%
3332
 
1.2%
4149
 
0.6%
668
 
0.3%
561
 
0.2%
744
 
0.2%
914
 
0.1%
Other values (5)36
 
0.1%
ValueCountFrequency (%)
08914
77.4%
-999991227
 
10.7%
1793
 
6.9%
2299
 
2.6%
3139
 
1.2%
477
 
0.7%
526
 
0.2%
620
 
0.2%
713
 
0.1%
94
 
< 0.1%
Other values (5)9
 
0.1%
ValueCountFrequency (%)
-999992986
 
11.1%
020703
77.0%
11822
 
6.8%
2666
 
2.5%
3332
 
1.2%
4149
 
0.6%
561
 
0.2%
668
 
0.3%
744
 
0.2%
814
 
0.1%
ValueCountFrequency (%)
-999991227
 
10.7%
08914
77.4%
1793
 
6.9%
2299
 
2.6%
3139
 
1.2%
477
 
0.7%
526
 
0.2%
620
 
0.2%
713
 
0.1%
84
 
< 0.1%
ValueCountFrequency (%)
-999991227
 
4.6%
08914
33.2%
1793
 
3.0%
2299
 
1.1%
3139
 
0.5%
477
 
0.3%
526
 
0.1%
620
 
0.1%
713
 
< 0.1%
84
 
< 0.1%
ValueCountFrequency (%)
-999992986
 
25.9%
020703
179.7%
11822
 
15.8%
2666
 
5.8%
3332
 
2.9%
4149
 
1.3%
561
 
0.5%
668
 
0.6%
744
 
0.4%
814
 
0.1%

CC_enq_L12m
Real number (ℝ)

 TrainTest
Distinct2118
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11107.708-10649.628
 TrainTest
Minimum-99999-99999
Maximum2416
Zeros194548393
Zeros (%)72.4%72.8%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q300
95-th percentile22
Maximum2416
Range100023100015
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation31423.79830848.962
Coefficient of variation (CV)-2.8290083-2.8967173
Kurtosis4.12829874.511242
Mean-11107.708-10649.628
Median Absolute Deviation (MAD)00
Skewness-2.475478-2.5515601
Sum-2.985863 × 108-1.2269436 × 108
Variance9.8745511 × 1089.5165845 × 108
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
019454
72.4%
-999992986
 
11.1%
12253
 
8.4%
2901
 
3.4%
3447
 
1.7%
4266
 
1.0%
5150
 
0.6%
6126
 
0.5%
789
 
0.3%
854
 
0.2%
Other values (11)155
 
0.6%
ValueCountFrequency (%)
08393
72.8%
-999991227
 
10.7%
1968
 
8.4%
2372
 
3.2%
3214
 
1.9%
4137
 
1.2%
564
 
0.6%
651
 
0.4%
726
 
0.2%
916
 
0.1%
Other values (8)53
 
0.5%
ValueCountFrequency (%)
-999992986
 
11.1%
019454
72.4%
12253
 
8.4%
2901
 
3.4%
3447
 
1.7%
4266
 
1.0%
5150
 
0.6%
6126
 
0.5%
789
 
0.3%
854
 
0.2%
ValueCountFrequency (%)
-999991227
 
10.7%
08393
72.8%
1968
 
8.4%
2372
 
3.2%
3214
 
1.9%
4137
 
1.2%
564
 
0.6%
651
 
0.4%
726
 
0.2%
815
 
0.1%
ValueCountFrequency (%)
-999991227
 
4.6%
08393
31.2%
1968
 
3.6%
2372
 
1.4%
3214
 
0.8%
4137
 
0.5%
564
 
0.2%
651
 
0.2%
726
 
0.1%
815
 
0.1%
ValueCountFrequency (%)
-999992986
 
25.9%
019454
168.9%
12253
 
19.6%
2901
 
7.8%
3447
 
3.9%
4266
 
2.3%
5150
 
1.3%
6126
 
1.1%
789
 
0.8%
854
 
0.5%

PL_enq
Real number (ℝ)

 TrainTest
Distinct3934
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11106.868-10648.837
 TrainTest
Minimum-99999-99999
Maximum4644
Zeros131445680
Zeros (%)48.9%49.3%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q311
95-th percentile66
Maximum4644
Range100045100043
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation31424.09630849.235
Coefficient of variation (CV)-2.8292491-2.8969582
Kurtosis4.12829864.5112419
Mean-11106.868-10648.837
Median Absolute Deviation (MAD)11
Skewness-2.475478-2.5515601
Sum-2.9856372 × 108-1.2268525 × 108
Variance9.8747378 × 1089.516753 × 108
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
013144
48.9%
14333
 
16.1%
-999992986
 
11.1%
22327
 
8.7%
31262
 
4.7%
4864
 
3.2%
5458
 
1.7%
6389
 
1.4%
7261
 
1.0%
8190
 
0.7%
Other values (29)667
 
2.5%
ValueCountFrequency (%)
05680
49.3%
11895
 
16.4%
-999991227
 
10.7%
21048
 
9.1%
3542
 
4.7%
4344
 
3.0%
5196
 
1.7%
6162
 
1.4%
7106
 
0.9%
872
 
0.6%
Other values (24)249
 
2.2%
ValueCountFrequency (%)
-999992986
 
11.1%
013144
48.9%
14333
 
16.1%
22327
 
8.7%
31262
 
4.7%
4864
 
3.2%
5458
 
1.7%
6389
 
1.4%
7261
 
1.0%
8190
 
0.7%
ValueCountFrequency (%)
-999991227
 
10.7%
05680
49.3%
11895
 
16.4%
21048
 
9.1%
3542
 
4.7%
4344
 
3.0%
5196
 
1.7%
6162
 
1.4%
7106
 
0.9%
872
 
0.6%
ValueCountFrequency (%)
-999991227
 
4.6%
05680
21.1%
11895
 
7.0%
21048
 
3.9%
3542
 
2.0%
4344
 
1.3%
5196
 
0.7%
6162
 
0.6%
7106
 
0.4%
872
 
0.3%
ValueCountFrequency (%)
-999992986
 
25.9%
013144
114.1%
14333
 
37.6%
22327
 
20.2%
31262
 
11.0%
4864
 
7.5%
5458
 
4.0%
6389
 
3.4%
7261
 
2.3%
8190
 
1.6%

PL_enq_L6m
Real number (ℝ)

 TrainTest
Distinct2723
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11107.592-10649.511
 TrainTest
Minimum-99999-99999
Maximum2844
Zeros175597558
Zeros (%)65.3%65.6%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q300
95-th percentile33
Maximum2844
Range100027100043
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation31423.83930849.002
Coefficient of variation (CV)-2.8290415-2.896753
Kurtosis4.12829874.511242
Mean-11107.592-10649.511
Median Absolute Deviation (MAD)00
Skewness-2.475478-2.5515601
Sum-2.9858319 × 108-1.2269301 × 108
Variance9.8745768 × 1089.5166094 × 108
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
017559
65.3%
13430
 
12.8%
-999992986
 
11.1%
21396
 
5.2%
3605
 
2.3%
4301
 
1.1%
5171
 
0.6%
6157
 
0.6%
772
 
0.3%
857
 
0.2%
Other values (17)147
 
0.5%
ValueCountFrequency (%)
07558
65.6%
11495
 
13.0%
-999991227
 
10.7%
2634
 
5.5%
3258
 
2.2%
4109
 
0.9%
583
 
0.7%
651
 
0.4%
831
 
0.3%
727
 
0.2%
Other values (13)48
 
0.4%
ValueCountFrequency (%)
-999992986
 
11.1%
017559
65.3%
13430
 
12.8%
21396
 
5.2%
3605
 
2.3%
4301
 
1.1%
5171
 
0.6%
6157
 
0.6%
772
 
0.3%
857
 
0.2%
ValueCountFrequency (%)
-999991227
 
10.7%
07558
65.6%
11495
 
13.0%
2634
 
5.5%
3258
 
2.2%
4109
 
0.9%
583
 
0.7%
651
 
0.4%
727
 
0.2%
831
 
0.3%
ValueCountFrequency (%)
-999991227
 
4.6%
07558
28.1%
11495
 
5.6%
2634
 
2.4%
3258
 
1.0%
4109
 
0.4%
583
 
0.3%
651
 
0.2%
727
 
0.1%
831
 
0.1%
ValueCountFrequency (%)
-999992986
 
25.9%
017559
152.4%
13430
 
29.8%
21396
 
12.1%
3605
 
5.3%
4301
 
2.6%
5171
 
1.5%
6157
 
1.4%
772
 
0.6%
857
 
0.5%

PL_enq_L12m
Real number (ℝ)

 TrainTest
Distinct3429
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11107.324-10649.25
 TrainTest
Minimum-99999-99999
Maximum4444
Zeros153346640
Zeros (%)57.0%57.6%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q311
95-th percentile44
Maximum4444
Range100043100043
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation31423.93430849.092
Coefficient of variation (CV)-2.8291185-2.8968325
Kurtosis4.12829864.511242
Mean-11107.324-10649.25
Median Absolute Deviation (MAD)00
Skewness-2.475478-2.5515601
Sum-2.9857597 × 108-1.2269001 × 108
Variance9.8746365 × 1089.516665 × 108
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
015334
57.0%
14269
 
15.9%
-999992986
 
11.1%
21830
 
6.8%
3958
 
3.6%
4483
 
1.8%
5262
 
1.0%
6213
 
0.8%
7184
 
0.7%
881
 
0.3%
Other values (24)281
 
1.0%
ValueCountFrequency (%)
06640
57.6%
11783
 
15.5%
-999991227
 
10.7%
2847
 
7.4%
3433
 
3.8%
4190
 
1.6%
5113
 
1.0%
675
 
0.7%
767
 
0.6%
836
 
0.3%
Other values (19)110
 
1.0%
ValueCountFrequency (%)
-999992986
 
11.1%
015334
57.0%
14269
 
15.9%
21830
 
6.8%
3958
 
3.6%
4483
 
1.8%
5262
 
1.0%
6213
 
0.8%
7184
 
0.7%
881
 
0.3%
ValueCountFrequency (%)
-999991227
 
10.7%
06640
57.6%
11783
 
15.5%
2847
 
7.4%
3433
 
3.8%
4190
 
1.6%
5113
 
1.0%
675
 
0.7%
767
 
0.6%
836
 
0.3%
ValueCountFrequency (%)
-999991227
 
4.6%
06640
24.7%
11783
 
6.6%
2847
 
3.2%
3433
 
1.6%
4190
 
0.7%
5113
 
0.4%
675
 
0.3%
767
 
0.2%
836
 
0.1%
ValueCountFrequency (%)
-999992986
 
25.9%
015334
133.1%
14269
 
37.1%
21830
 
15.9%
3958
 
8.3%
4483
 
4.2%
5262
 
2.3%
6213
 
1.8%
7184
 
1.6%
881
 
0.7%

time_since_recent_enq
Real number (ℝ)

 TrainTest
Distinct17411337
Distinct (%)6.5%11.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-10891.726-10429.566
 TrainTest
Minimum-99999-99999
Maximum47684208
Zeros23799
Zeros (%)0.9%0.9%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:33.706444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q144
median4445
Q3232240
95-th percentile10531066
Maximum47684208
Range104767104207
Interquartile range (IQR)228236

Descriptive statistics

 TrainTest
Standard deviation31502.93630927.788
Coefficient of variation (CV)-2.8923731-2.9653954
Kurtosis4.12592364.5086219
Mean-10891.726-10429.566
Median Absolute Deviation (MAD)5554
Skewness-2.4746247-2.5506556
Sum-2.9278049 × 108-1.2015903 × 108
Variance9.9243497 × 1089.5652804 × 108
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:33.821617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999992986
 
11.1%
11253
 
4.7%
21151
 
4.3%
3942
 
3.5%
4709
 
2.6%
5557
 
2.1%
6516
 
1.9%
7403
 
1.5%
8310
 
1.2%
9302
 
1.1%
Other values (1731)17752
66.0%
ValueCountFrequency (%)
-999991227
 
10.7%
2540
 
4.7%
1500
 
4.3%
3409
 
3.6%
4297
 
2.6%
6218
 
1.9%
5212
 
1.8%
7186
 
1.6%
8147
 
1.3%
9138
 
1.2%
Other values (1327)7647
66.4%
ValueCountFrequency (%)
-999992986
11.1%
0237
 
0.9%
11253
4.7%
21151
 
4.3%
3942
 
3.5%
4709
 
2.6%
5557
 
2.1%
6516
 
1.9%
7403
 
1.5%
8310
 
1.2%
ValueCountFrequency (%)
-999991227
10.7%
099
 
0.9%
1500
4.3%
2540
4.7%
3409
 
3.6%
4297
 
2.6%
5212
 
1.8%
6218
 
1.9%
7186
 
1.6%
8147
 
1.3%
ValueCountFrequency (%)
-999991227
4.6%
099
 
0.4%
1500
1.9%
2540
2.0%
3409
 
1.5%
4297
 
1.1%
5212
 
0.8%
6218
 
0.8%
7186
 
0.7%
8147
 
0.5%
ValueCountFrequency (%)
-999992986
25.9%
0237
 
2.1%
11253
10.9%
21151
 
10.0%
3942
 
8.2%
4709
 
6.2%
5557
 
4.8%
6516
 
4.5%
7403
 
3.5%
8310
 
2.7%

enq_L12m
Real number (ℝ)

 TrainTest
Distinct6154
Distinct (%)0.2%0.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11105.028-10647.021
 TrainTest
Minimum-99999-99999
Maximum8787
Zeros46432064
Zeros (%)17.3%17.9%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:33.926392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median22
Q344
95-th percentile1211
Maximum8787
Range100086100086
Interquartile range (IQR)44

Descriptive statistics

 TrainTest
Standard deviation31424.74630849.862
Coefficient of variation (CV)-2.8297764-2.8975112
Kurtosis4.12829844.5112417
Mean-11105.028-10647.021
Median Absolute Deviation (MAD)22
Skewness-2.4754779-2.55156
Sum-2.9851426 × 108-1.2266433 × 108
Variance9.8751467 × 1089.51714 × 108
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:34.033237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15664
21.1%
04643
17.3%
23682
13.7%
-999992986
11.1%
32560
9.5%
41788
 
6.7%
51222
 
4.5%
6856
 
3.2%
7670
 
2.5%
8510
 
1.9%
Other values (51)2300
8.6%
ValueCountFrequency (%)
12404
20.9%
02064
17.9%
21570
13.6%
-999991227
10.7%
31136
9.9%
4828
 
7.2%
5523
 
4.5%
6374
 
3.2%
7247
 
2.1%
8213
 
1.8%
Other values (44)935
 
8.1%
ValueCountFrequency (%)
-999992986
11.1%
04643
17.3%
15664
21.1%
23682
13.7%
32560
9.5%
41788
 
6.7%
51222
 
4.5%
6856
 
3.2%
7670
 
2.5%
8510
 
1.9%
ValueCountFrequency (%)
-999991227
10.7%
02064
17.9%
12404
20.9%
21570
13.6%
31136
9.9%
4828
 
7.2%
5523
 
4.5%
6374
 
3.2%
7247
 
2.1%
8213
 
1.8%
ValueCountFrequency (%)
-999991227
4.6%
02064
7.7%
12404
8.9%
21570
5.8%
31136
4.2%
4828
 
3.1%
5523
 
1.9%
6374
 
1.4%
7247
 
0.9%
8213
 
0.8%
ValueCountFrequency (%)
-999992986
25.9%
04643
40.3%
15664
49.2%
23682
32.0%
32560
22.2%
41788
 
15.5%
51222
 
10.6%
6856
 
7.4%
7670
 
5.8%
8510
 
4.4%

enq_L6m
Real number (ℝ)

 TrainTest
Distinct4944
Distinct (%)0.2%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11106.098-10648.064
 TrainTest
Minimum-99999-99999
Maximum6666
Zeros78853426
Zeros (%)29.3%29.7%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median11
Q332
95-th percentile87
Maximum6666
Range100065100065
Interquartile range (IQR)32

Descriptive statistics

 TrainTest
Standard deviation31424.36830849.502
Coefficient of variation (CV)-2.8294697-2.8971934
Kurtosis4.12829854.5112419
Mean-11106.098-10648.064
Median Absolute Deviation (MAD)11
Skewness-2.475478-2.55156
Sum-2.9854302 × 108-1.2267635 × 108
Variance9.8749089 × 1089.5169176 × 108
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
07885
29.3%
15813
21.6%
23367
12.5%
-999992986
 
11.1%
32174
 
8.1%
41311
 
4.9%
5852
 
3.2%
6612
 
2.3%
7426
 
1.6%
8298
 
1.1%
Other values (39)1157
 
4.3%
ValueCountFrequency (%)
03426
29.7%
12491
21.6%
21500
13.0%
-999991227
 
10.7%
3920
 
8.0%
4583
 
5.1%
5354
 
3.1%
6258
 
2.2%
7188
 
1.6%
8116
 
1.0%
Other values (34)458
 
4.0%
ValueCountFrequency (%)
-999992986
 
11.1%
07885
29.3%
15813
21.6%
23367
12.5%
32174
 
8.1%
41311
 
4.9%
5852
 
3.2%
6612
 
2.3%
7426
 
1.6%
8298
 
1.1%
ValueCountFrequency (%)
-999991227
 
10.7%
03426
29.7%
12491
21.6%
21500
13.0%
3920
 
8.0%
4583
 
5.1%
5354
 
3.1%
6258
 
2.2%
7188
 
1.6%
8116
 
1.0%
ValueCountFrequency (%)
-999991227
 
4.6%
03426
12.7%
12491
9.3%
21500
5.6%
3920
 
3.4%
4583
 
2.2%
5354
 
1.3%
6258
 
1.0%
7188
 
0.7%
8116
 
0.4%
ValueCountFrequency (%)
-999992986
 
25.9%
07885
68.4%
15813
50.5%
23367
29.2%
32174
 
18.9%
41311
 
11.4%
5852
 
7.4%
6612
 
5.3%
7426
 
3.7%
8298
 
2.6%

enq_L3m
Real number (ℝ)

 TrainTest
Distinct3528
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-11106.883-10648.822
 TrainTest
Minimum-99999-99999
Maximum4235
Zeros107564675
Zeros (%)40.0%40.6%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q100
median00
Q322
95-th percentile55
Maximum4235
Range100041100034
Interquartile range (IQR)22

Descriptive statistics

 TrainTest
Standard deviation31424.0930849.24
Coefficient of variation (CV)-2.8292448-2.8969628
Kurtosis4.12829864.511242
Mean-11106.883-10648.822
Median Absolute Deviation (MAD)11
Skewness-2.475478-2.5515601
Sum-2.9856412 × 108-1.2268507 × 108
Variance9.8747345 × 1089.5167562 × 108
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
010756
40.0%
16049
22.5%
22992
 
11.1%
-999992986
 
11.1%
31654
 
6.2%
4921
 
3.4%
5448
 
1.7%
6311
 
1.2%
7213
 
0.8%
8125
 
0.5%
Other values (25)426
 
1.6%
ValueCountFrequency (%)
04675
40.6%
12592
22.5%
21302
 
11.3%
-999991227
 
10.7%
3702
 
6.1%
4394
 
3.4%
5186
 
1.6%
6142
 
1.2%
773
 
0.6%
865
 
0.6%
Other values (18)163
 
1.4%
ValueCountFrequency (%)
-999992986
 
11.1%
010756
40.0%
16049
22.5%
22992
 
11.1%
31654
 
6.2%
4921
 
3.4%
5448
 
1.7%
6311
 
1.2%
7213
 
0.8%
8125
 
0.5%
ValueCountFrequency (%)
-999991227
 
10.7%
04675
40.6%
12592
22.5%
21302
 
11.3%
3702
 
6.1%
4394
 
3.4%
5186
 
1.6%
6142
 
1.2%
773
 
0.6%
865
 
0.6%
ValueCountFrequency (%)
-999991227
 
4.6%
04675
17.4%
12592
9.6%
21302
 
4.8%
3702
 
2.6%
4394
 
1.5%
5186
 
0.7%
6142
 
0.5%
773
 
0.3%
865
 
0.2%
ValueCountFrequency (%)
-999992986
 
25.9%
010756
93.4%
16049
52.5%
22992
 
26.0%
31654
 
14.4%
4921
 
8.0%
5448
 
3.9%
6311
 
2.7%
7213
 
1.8%
8125
 
1.1%

MARITALSTATUS
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
Married
19835 
Single
7046 
Married
8378 
Single
3143 

Length

 TrainTest
Max length77
Median length77
Mean length6.73788186.7271938
Min length66

Characters and Unicode

 TrainTest
Total characters18112177504
Distinct characters1010
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowMarriedMarried
2nd rowMarriedSingle
3rd rowMarriedMarried
4th rowSingleMarried
5th rowMarriedMarried

Common Values

ValueCountFrequency (%)
Married19835
73.8%
Single7046
 
26.2%
ValueCountFrequency (%)
Married8378
72.7%
Single3143
 
27.3%

Length

2026-01-17T22:57:34.115567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:34.151652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:34.178779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married19835
73.8%
single7046
 
26.2%
ValueCountFrequency (%)
married8378
72.7%
single3143
 
27.3%

Most occurring characters

ValueCountFrequency (%)
r39670
21.9%
i26881
14.8%
e26881
14.8%
M19835
11.0%
a19835
11.0%
d19835
11.0%
S7046
 
3.9%
n7046
 
3.9%
g7046
 
3.9%
l7046
 
3.9%
ValueCountFrequency (%)
r16756
21.6%
i11521
14.9%
e11521
14.9%
M8378
10.8%
a8378
10.8%
d8378
10.8%
S3143
 
4.1%
n3143
 
4.1%
g3143
 
4.1%
l3143
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)181121
100.0%
ValueCountFrequency (%)
(unknown)77504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r39670
21.9%
i26881
14.8%
e26881
14.8%
M19835
11.0%
a19835
11.0%
d19835
11.0%
S7046
 
3.9%
n7046
 
3.9%
g7046
 
3.9%
l7046
 
3.9%
ValueCountFrequency (%)
r16756
21.6%
i11521
14.9%
e11521
14.9%
M8378
10.8%
a8378
10.8%
d8378
10.8%
S3143
 
4.1%
n3143
 
4.1%
g3143
 
4.1%
l3143
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)181121
100.0%
ValueCountFrequency (%)
(unknown)77504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r39670
21.9%
i26881
14.8%
e26881
14.8%
M19835
11.0%
a19835
11.0%
d19835
11.0%
S7046
 
3.9%
n7046
 
3.9%
g7046
 
3.9%
l7046
 
3.9%
ValueCountFrequency (%)
r16756
21.6%
i11521
14.9%
e11521
14.9%
M8378
10.8%
a8378
10.8%
d8378
10.8%
S3143
 
4.1%
n3143
 
4.1%
g3143
 
4.1%
l3143
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)181121
100.0%
ValueCountFrequency (%)
(unknown)77504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r39670
21.9%
i26881
14.8%
e26881
14.8%
M19835
11.0%
a19835
11.0%
d19835
11.0%
S7046
 
3.9%
n7046
 
3.9%
g7046
 
3.9%
l7046
 
3.9%
ValueCountFrequency (%)
r16756
21.6%
i11521
14.9%
e11521
14.9%
M8378
10.8%
a8378
10.8%
d8378
10.8%
S3143
 
4.1%
n3143
 
4.1%
g3143
 
4.1%
l3143
 
4.1%

EDUCATION
Categorical

 TrainTest
Distinct77
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
GRADUATE
9106 
12TH
7431 
SSC
4574 
UNDER GRADUATE
2844 
OTHERS
1453 
Other values (2)
1473 
GRADUATE
3950 
12TH
3111 
SSC
1976 
UNDER GRADUATE
1258 
OTHERS
628 
Other values (2)
598 

Length

 TrainTest
Max length1414
Median length1313
Mean length6.83888256.8624251
Min length33

Characters and Unicode

 TrainTest
Total characters18383679062
Distinct characters2020
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowSSCOTHERS
2nd rowGRADUATE12TH
3rd rowSSCGRADUATE
4th rowGRADUATESSC
5th rowOTHERSGRADUATE

Common Values

ValueCountFrequency (%)
GRADUATE9106
33.9%
12TH7431
27.6%
SSC4574
17.0%
UNDER GRADUATE2844
 
10.6%
OTHERS1453
 
5.4%
POST-GRADUATE1332
 
5.0%
PROFESSIONAL141
 
0.5%
ValueCountFrequency (%)
GRADUATE3950
34.3%
12TH3111
27.0%
SSC1976
17.2%
UNDER GRADUATE1258
 
10.9%
OTHERS628
 
5.5%
POST-GRADUATE534
 
4.6%
PROFESSIONAL64
 
0.6%

Length

2026-01-17T22:57:34.229743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:34.281878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:34.342465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate11950
40.2%
12th7431
25.0%
ssc4574
 
15.4%
under2844
 
9.6%
others1453
 
4.9%
post-graduate1332
 
4.5%
professional141
 
0.5%
ValueCountFrequency (%)
graduate5208
40.8%
12th3111
24.3%
ssc1976
 
15.5%
under1258
 
9.8%
others628
 
4.9%
post-graduate534
 
4.2%
professional64
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A26705
14.5%
T23498
12.8%
E17720
9.6%
R17720
9.6%
U16126
8.8%
D16126
8.8%
G13282
7.2%
S12215
6.6%
H8884
 
4.8%
17431
 
4.0%
Other values (10)24129
13.1%
ValueCountFrequency (%)
A11548
14.6%
T10015
12.7%
E7692
9.7%
R7692
9.7%
U7000
8.9%
D7000
8.9%
G5742
7.3%
S5242
6.6%
H3739
 
4.7%
13111
 
3.9%
Other values (10)10281
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)183836
100.0%
ValueCountFrequency (%)
(unknown)79062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A26705
14.5%
T23498
12.8%
E17720
9.6%
R17720
9.6%
U16126
8.8%
D16126
8.8%
G13282
7.2%
S12215
6.6%
H8884
 
4.8%
17431
 
4.0%
Other values (10)24129
13.1%
ValueCountFrequency (%)
A11548
14.6%
T10015
12.7%
E7692
9.7%
R7692
9.7%
U7000
8.9%
D7000
8.9%
G5742
7.3%
S5242
6.6%
H3739
 
4.7%
13111
 
3.9%
Other values (10)10281
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)183836
100.0%
ValueCountFrequency (%)
(unknown)79062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A26705
14.5%
T23498
12.8%
E17720
9.6%
R17720
9.6%
U16126
8.8%
D16126
8.8%
G13282
7.2%
S12215
6.6%
H8884
 
4.8%
17431
 
4.0%
Other values (10)24129
13.1%
ValueCountFrequency (%)
A11548
14.6%
T10015
12.7%
E7692
9.7%
R7692
9.7%
U7000
8.9%
D7000
8.9%
G5742
7.3%
S5242
6.6%
H3739
 
4.7%
13111
 
3.9%
Other values (10)10281
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)183836
100.0%
ValueCountFrequency (%)
(unknown)79062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A26705
14.5%
T23498
12.8%
E17720
9.6%
R17720
9.6%
U16126
8.8%
D16126
8.8%
G13282
7.2%
S12215
6.6%
H8884
 
4.8%
17431
 
4.0%
Other values (10)24129
13.1%
ValueCountFrequency (%)
A11548
14.6%
T10015
12.7%
E7692
9.7%
R7692
9.7%
U7000
8.9%
D7000
8.9%
G5742
7.3%
S5242
6.6%
H3739
 
4.7%
13111
 
3.9%
Other values (10)10281
13.0%

AGE
Real number (ℝ)

 TrainTest
Distinct4645
Distinct (%)0.2%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean33.75901233.65654
 TrainTest
Minimum2121
Maximum7765
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum2121
5-th percentile2222
Q12727
median3232
Q33939
95-th percentile5151
Maximum7765
Range5644
Interquartile range (IQR)1212

Descriptive statistics

 TrainTest
Standard deviation8.7465198.7561582
Coefficient of variation (CV)0.259086940.26016216
Kurtosis0.0387391910.066933517
Mean33.75901233.65654
Median Absolute Deviation (MAD)66
Skewness0.777989370.79024104
Sum907476387757
Variance76.50159576.670306
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
271373
 
5.1%
291349
 
5.0%
281326
 
4.9%
301323
 
4.9%
251293
 
4.8%
261252
 
4.7%
311215
 
4.5%
321203
 
4.5%
241186
 
4.4%
331180
 
4.4%
Other values (36)14181
52.8%
ValueCountFrequency (%)
29615
 
5.3%
27583
 
5.1%
28558
 
4.8%
30547
 
4.7%
26545
 
4.7%
24544
 
4.7%
31537
 
4.7%
32527
 
4.6%
25518
 
4.5%
33473
 
4.1%
Other values (35)6074
52.7%
ValueCountFrequency (%)
21554
2.1%
22838
3.1%
231026
3.8%
241186
4.4%
251293
4.8%
261252
4.7%
271373
5.1%
281326
4.9%
291349
5.0%
301323
4.9%
ValueCountFrequency (%)
21242
 
2.1%
22376
3.3%
23472
4.1%
24544
4.7%
25518
4.5%
26545
4.7%
27583
5.1%
28558
4.8%
29615
5.3%
30547
4.7%
ValueCountFrequency (%)
21242
 
0.9%
22376
1.4%
23472
1.8%
24544
2.0%
25518
1.9%
26545
2.0%
27583
2.2%
28558
2.1%
29615
2.3%
30547
2.0%
ValueCountFrequency (%)
21554
4.8%
22838
7.3%
231026
8.9%
241186
10.3%
251293
11.2%
261252
10.9%
271373
11.9%
281326
11.5%
291349
11.7%
301323
11.5%

GENDER
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
M
23875 
F
3006 
M
10191 
F
1330 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowMF
2nd rowFM
3rd rowMM
4th rowMM
5th rowFM

Common Values

ValueCountFrequency (%)
M23875
88.8%
F3006
 
11.2%
ValueCountFrequency (%)
M10191
88.5%
F1330
 
11.5%

Length

2026-01-17T22:57:34.415656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:34.456789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:34.482481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m23875
88.8%
f3006
 
11.2%
ValueCountFrequency (%)
m10191
88.5%
f1330
 
11.5%

Most occurring characters

ValueCountFrequency (%)
M23875
88.8%
F3006
 
11.2%
ValueCountFrequency (%)
M10191
88.5%
F1330
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M23875
88.8%
F3006
 
11.2%
ValueCountFrequency (%)
M10191
88.5%
F1330
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M23875
88.8%
F3006
 
11.2%
ValueCountFrequency (%)
M10191
88.5%
F1330
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M23875
88.8%
F3006
 
11.2%
ValueCountFrequency (%)
M10191
88.5%
F1330
 
11.5%

NETMONTHLYINCOME
Real number (ℝ)

 TrainTest
Distinct738479
Distinct (%)2.7%4.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean27175.48227298.744
 TrainTest
Minimum00
Maximum7000002500000
Zeros1711
Zeros (%)0.1%0.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:34.551631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile1200012000
Q11800018000
median2400024000
Q33200032000
95-th percentile5000050000
Maximum7000002500000
Range7000002500000
Interquartile range (IQR)1400014000

Descriptive statistics

 TrainTest
Standard deviation19599.82529141.73
Coefficient of variation (CV)0.721231911.0675117
Kurtosis280.505114524.4074
Mean27175.48227298.744
Median Absolute Deviation (MAD)60006000
Skewness11.78931855.070414
Sum7.3050412 × 1083.1450883 × 108
Variance3.8415312 × 1088.4924044 × 108
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:34.664383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200002496
 
9.3%
250002427
 
9.0%
300002105
 
7.8%
150001871
 
7.0%
180001538
 
5.7%
350001319
 
4.9%
400001051
 
3.9%
22000785
 
2.9%
45000624
 
2.3%
28000615
 
2.3%
Other values (728)12050
44.8%
ValueCountFrequency (%)
200001142
 
9.9%
250001062
 
9.2%
30000876
 
7.6%
15000790
 
6.9%
18000631
 
5.5%
35000575
 
5.0%
40000449
 
3.9%
22000311
 
2.7%
45000274
 
2.4%
28000261
 
2.3%
Other values (469)5150
44.7%
ValueCountFrequency (%)
017
0.1%
14
 
< 0.1%
24
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
1036
0.1%
114
 
< 0.1%
ValueCountFrequency (%)
011
0.1%
51
 
< 0.1%
91
 
< 0.1%
1010
0.1%
126
0.1%
154
 
< 0.1%
181
 
< 0.1%
201
 
< 0.1%
221
 
< 0.1%
251
 
< 0.1%
ValueCountFrequency (%)
011
< 0.1%
51
 
< 0.1%
91
 
< 0.1%
1010
< 0.1%
126
< 0.1%
154
 
< 0.1%
181
 
< 0.1%
201
 
< 0.1%
221
 
< 0.1%
251
 
< 0.1%
ValueCountFrequency (%)
017
0.1%
14
 
< 0.1%
24
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
1036
0.3%
114
 
< 0.1%

Time_With_Curr_Empr
Real number (ℝ)

 TrainTest
Distinct426379
Distinct (%)1.6%3.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean109.91005110.00373
 TrainTest
Minimum03
Maximum1020839
Zeros10
Zeros (%)< 0.1%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:34.784059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum03
5-th percentile2727
Q16160
median9090
Q3131131
95-th percentile250251
Maximum1020839
Range1020836
Interquartile range (IQR)7071

Descriptive statistics

 TrainTest
Standard deviation75.77186576.92445
Coefficient of variation (CV)0.689398890.69928946
Kurtosis6.76916525.912624
Mean109.91005110.00373
Median Absolute Deviation (MAD)3737
Skewness1.86758461.8571038
Sum29544921267353
Variance5741.37555917.3711
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:34.892392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66866
 
3.2%
125789
 
2.9%
126662
 
2.5%
71494
 
1.8%
62453
 
1.7%
65445
 
1.7%
102381
 
1.4%
30379
 
1.4%
130370
 
1.4%
77368
 
1.4%
Other values (416)21674
80.6%
ValueCountFrequency (%)
66369
 
3.2%
125344
 
3.0%
126279
 
2.4%
71193
 
1.7%
65181
 
1.6%
62176
 
1.5%
77174
 
1.5%
63170
 
1.5%
42163
 
1.4%
130162
 
1.4%
Other values (369)9310
80.8%
ValueCountFrequency (%)
01
 
< 0.1%
33
 
< 0.1%
56
 
< 0.1%
66
 
< 0.1%
91
 
< 0.1%
1238
 
0.1%
1373
0.3%
1496
0.4%
1552
0.2%
1615
 
0.1%
ValueCountFrequency (%)
32
 
< 0.1%
41
 
< 0.1%
53
 
< 0.1%
63
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
1222
0.2%
1333
0.3%
1445
0.4%
1526
0.2%
ValueCountFrequency (%)
32
 
< 0.1%
41
 
< 0.1%
53
 
< 0.1%
63
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
1222
0.1%
1333
0.1%
1445
0.2%
1526
0.1%
ValueCountFrequency (%)
01
 
< 0.1%
33
 
< 0.1%
56
 
0.1%
66
 
0.1%
91
 
< 0.1%
1238
 
0.3%
1373
0.6%
1496
0.8%
1552
0.5%
1615
 
0.1%

pct_of_active_TLs_ever
Real number (ℝ)

 TrainTest
Distinct332265
Distinct (%)1.2%2.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.58464190.58885383
 TrainTest
Minimum00
Maximum11
Zeros40771682
Zeros (%)15.2%14.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:34.996814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q10.2860.286
median0.5950.6
Q311
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.7140.714

Descriptive statistics

 TrainTest
Standard deviation0.372464410.369496
Coefficient of variation (CV)0.637081270.62748339
Kurtosis-1.3575591-1.3313392
Mean0.58464190.58885383
Median Absolute Deviation (MAD)0.4050.4
Skewness-0.24785968-0.26136069
Sum15715.7596784.185
Variance0.138729730.13652729
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:35.098609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19440
35.1%
04077
15.2%
0.52915
 
10.8%
0.6671440
 
5.4%
0.3331229
 
4.6%
0.75676
 
2.5%
0.25602
 
2.2%
0.6430
 
1.6%
0.4430
 
1.6%
0.2348
 
1.3%
Other values (322)5294
19.7%
ValueCountFrequency (%)
14047
35.1%
01682
14.6%
0.51318
 
11.4%
0.667601
 
5.2%
0.333503
 
4.4%
0.25279
 
2.4%
0.75278
 
2.4%
0.6223
 
1.9%
0.4198
 
1.7%
0.8151
 
1.3%
Other values (255)2241
19.5%
ValueCountFrequency (%)
04077
15.2%
0.0161
 
< 0.1%
0.022
 
< 0.1%
0.0212
 
< 0.1%
0.0221
 
< 0.1%
0.0252
 
< 0.1%
0.0261
 
< 0.1%
0.0271
 
< 0.1%
0.0293
 
< 0.1%
0.0314
 
< 0.1%
ValueCountFrequency (%)
01682
14.6%
0.0161
 
< 0.1%
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.021
 
< 0.1%
0.0211
 
< 0.1%
0.0241
 
< 0.1%
0.0251
 
< 0.1%
0.0281
 
< 0.1%
0.0291
 
< 0.1%
ValueCountFrequency (%)
01682
6.3%
0.0161
 
< 0.1%
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.021
 
< 0.1%
0.0211
 
< 0.1%
0.0241
 
< 0.1%
0.0251
 
< 0.1%
0.0281
 
< 0.1%
0.0291
 
< 0.1%
ValueCountFrequency (%)
04077
35.4%
0.0161
 
< 0.1%
0.022
 
< 0.1%
0.0212
 
< 0.1%
0.0221
 
< 0.1%
0.0252
 
< 0.1%
0.0261
 
< 0.1%
0.0271
 
< 0.1%
0.0293
 
< 0.1%
0.0314
 
< 0.1%

pct_opened_TLs_L6m_of_L12m
Real number (ℝ)

 TrainTest
Distinct8668
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.312500280.30968796
 TrainTest
Minimum00
Maximum11
Zeros154216654
Zeros (%)57.4%57.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:35.199349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q30.6670.667
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.6670.667

Descriptive statistics

 TrainTest
Standard deviation0.403800480.40315285
Coefficient of variation (CV)1.29216041.3018034
Kurtosis-1.0729818-1.0492012
Mean0.312500280.30968796
Median Absolute Deviation (MAD)00
Skewness0.777361120.79264279
Sum8400.323567.915
Variance0.163054830.16253222
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:35.305179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015421
57.4%
15260
 
19.6%
0.52375
 
8.8%
0.667833
 
3.1%
0.333743
 
2.8%
0.25323
 
1.2%
0.75316
 
1.2%
0.6229
 
0.9%
0.4211
 
0.8%
0.8134
 
0.5%
Other values (76)1036
 
3.9%
ValueCountFrequency (%)
06654
57.8%
12238
 
19.4%
0.51002
 
8.7%
0.667338
 
2.9%
0.333328
 
2.8%
0.75140
 
1.2%
0.25131
 
1.1%
0.4103
 
0.9%
0.688
 
0.8%
0.864
 
0.6%
Other values (58)435
 
3.8%
ValueCountFrequency (%)
015421
57.4%
0.0712
 
< 0.1%
0.1111
 
< 0.1%
0.1181
 
< 0.1%
0.1259
 
< 0.1%
0.1331
 
< 0.1%
0.14345
 
0.2%
0.1544
 
< 0.1%
0.16745
 
0.2%
0.1823
 
< 0.1%
ValueCountFrequency (%)
06654
57.8%
0.1257
 
0.1%
0.14313
 
0.1%
0.1542
 
< 0.1%
0.16719
 
0.2%
0.1823
 
< 0.1%
0.247
 
0.4%
0.2142
 
< 0.1%
0.2224
 
< 0.1%
0.25131
 
1.1%
ValueCountFrequency (%)
06654
24.8%
0.1257
 
< 0.1%
0.14313
 
< 0.1%
0.1542
 
< 0.1%
0.16719
 
0.1%
0.1823
 
< 0.1%
0.247
 
0.2%
0.2142
 
< 0.1%
0.2224
 
< 0.1%
0.25131
 
0.5%
ValueCountFrequency (%)
015421
133.9%
0.0712
 
< 0.1%
0.1111
 
< 0.1%
0.1181
 
< 0.1%
0.1259
 
0.1%
0.1331
 
< 0.1%
0.14345
 
0.4%
0.1544
 
< 0.1%
0.16745
 
0.4%
0.1823
 
< 0.1%

pct_currentBal_all_TL
Real number (ℝ)

 TrainTest
Distinct13691240
Distinct (%)5.1%10.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-110.65685-129.62879
 TrainTest
Minimum-99999-99999
Maximum6327.523.154
Zeros56152362
Zeros (%)20.9%20.5%
Negative3015
Negative (%)0.1%0.1%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:35.408027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile00
Q10.1660.171
median0.6380.646
Q30.890.892
95-th percentile1.0271.027
Maximum6327.523.154
Range106326.5100022.15
Interquartile range (IQR)0.7240.721

Descriptive statistics

 TrainTest
Standard deviation3339.15173606.0738
Coefficient of variation (CV)-30.175733-27.818464
Kurtosis890.93036763.39973
Mean-110.65685-129.62879
Median Absolute Deviation (MAD)0.3050.302
Skewness-29.87829-27.663463
Sum-2974566.9-1493453.3
Variance1114993413003768
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:35.510898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05615
 
20.9%
11330
 
4.9%
0.5189
 
0.7%
0.333116
 
0.4%
0.667112
 
0.4%
0.7103
 
0.4%
0.692
 
0.3%
0.491
 
0.3%
0.385
 
0.3%
1.00272
 
0.3%
Other values (1359)19076
71.0%
ValueCountFrequency (%)
02362
 
20.5%
1580
 
5.0%
0.592
 
0.8%
0.351
 
0.4%
0.644
 
0.4%
0.33344
 
0.4%
0.66743
 
0.4%
0.83341
 
0.4%
0.41737
 
0.3%
0.236
 
0.3%
Other values (1230)8191
71.1%
ValueCountFrequency (%)
-9999930
 
0.1%
05615
20.9%
0.00125
 
0.1%
0.00210
 
< 0.1%
0.0037
 
< 0.1%
0.0045
 
< 0.1%
0.0054
 
< 0.1%
0.0068
 
< 0.1%
0.0077
 
< 0.1%
0.0083
 
< 0.1%
ValueCountFrequency (%)
-9999915
 
0.1%
02362
20.5%
0.0015
 
< 0.1%
0.0027
 
0.1%
0.0032
 
< 0.1%
0.0041
 
< 0.1%
0.0052
 
< 0.1%
0.0062
 
< 0.1%
0.0074
 
< 0.1%
0.0081
 
< 0.1%
ValueCountFrequency (%)
-9999915
 
0.1%
02362
8.8%
0.0015
 
< 0.1%
0.0027
 
< 0.1%
0.0032
 
< 0.1%
0.0041
 
< 0.1%
0.0052
 
< 0.1%
0.0062
 
< 0.1%
0.0074
 
< 0.1%
0.0081
 
< 0.1%
ValueCountFrequency (%)
-9999930
 
0.3%
05615
48.7%
0.00125
 
0.2%
0.00210
 
0.1%
0.0037
 
0.1%
0.0045
 
< 0.1%
0.0054
 
< 0.1%
0.0068
 
0.1%
0.0077
 
0.1%
0.0083
 
< 0.1%

CC_utilization
Real number (ℝ)

 TrainTest
Distinct792618
Distinct (%)2.9%5.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-82908.919-82899.847
 TrainTest
Minimum-99999-99999
Maximum11
Zeros267119
Zeros (%)1.0%1.0%
Negative222879551
Negative (%)82.9%82.9%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:35.613651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q1-99999-99999
median-99999-99999
Q3-99999-99999
95-th percentile0.9580.953
Maximum11
Range100000100000
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation37642.85137651.714
Coefficient of variation (CV)-0.45402656-0.45418315
Kurtosis1.05787691.0554632
Mean-82908.919-82899.847
Median Absolute Deviation (MAD)00
Skewness1.74865611.7479359
Sum-2.2286746 × 109-9.5508914 × 108
Variance1.4169843 × 1091.4176516 × 109
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:35.728900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9999922287
82.9%
1932
 
3.5%
0267
 
1.0%
0.00132
 
0.1%
0.91328
 
0.1%
0.99125
 
0.1%
0.99922
 
0.1%
0.96218
 
0.1%
0.95817
 
0.1%
0.96717
 
0.1%
Other values (782)3236
 
12.0%
ValueCountFrequency (%)
-999999551
82.9%
1383
 
3.3%
0119
 
1.0%
0.96712
 
0.1%
0.95812
 
0.1%
0.94812
 
0.1%
0.99110
 
0.1%
0.93710
 
0.1%
0.0049
 
0.1%
0.9258
 
0.1%
Other values (608)1395
 
12.1%
ValueCountFrequency (%)
-9999922287
82.9%
0267
 
1.0%
0.00132
 
0.1%
0.00213
 
< 0.1%
0.0033
 
< 0.1%
0.0047
 
< 0.1%
0.0052
 
< 0.1%
0.00613
 
< 0.1%
0.0078
 
< 0.1%
0.0083
 
< 0.1%
ValueCountFrequency (%)
-999999551
82.9%
0119
 
1.0%
0.0017
 
0.1%
0.0025
 
< 0.1%
0.0031
 
< 0.1%
0.0049
 
0.1%
0.0053
 
< 0.1%
0.0066
 
0.1%
0.0071
 
< 0.1%
0.0082
 
< 0.1%
ValueCountFrequency (%)
-999999551
35.5%
0119
 
0.4%
0.0017
 
< 0.1%
0.0025
 
< 0.1%
0.0031
 
< 0.1%
0.0049
 
< 0.1%
0.0053
 
< 0.1%
0.0066
 
< 0.1%
0.0071
 
< 0.1%
0.0082
 
< 0.1%
ValueCountFrequency (%)
-9999922287
193.4%
0267
 
2.3%
0.00132
 
0.3%
0.00213
 
0.1%
0.0033
 
< 0.1%
0.0047
 
0.1%
0.0052
 
< 0.1%
0.00613
 
0.1%
0.0078
 
0.1%
0.0083
 
< 0.1%

CC_Flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
21879 
1
5002 
0
9375 
1
2146 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
021879
81.4%
15002
 
18.6%
ValueCountFrequency (%)
09375
81.4%
12146
 
18.6%

Length

2026-01-17T22:57:35.820026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:35.861093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:35.888706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
021879
81.4%
15002
 
18.6%
ValueCountFrequency (%)
09375
81.4%
12146
 
18.6%

Most occurring characters

ValueCountFrequency (%)
021879
81.4%
15002
 
18.6%
ValueCountFrequency (%)
09375
81.4%
12146
 
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
021879
81.4%
15002
 
18.6%
ValueCountFrequency (%)
09375
81.4%
12146
 
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
021879
81.4%
15002
 
18.6%
ValueCountFrequency (%)
09375
81.4%
12146
 
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
021879
81.4%
15002
 
18.6%
ValueCountFrequency (%)
09375
81.4%
12146
 
18.6%

PL_utilization
Real number (ℝ)

 TrainTest
Distinct743571
Distinct (%)2.8%5.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-84170.015-84540.311
 TrainTest
Minimum-99999-99999
Maximum1.3941.394
Zeros10746
Zeros (%)0.4%0.4%
Negative226269740
Negative (%)84.2%84.5%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:35.962881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q1-99999-99999
median-99999-99999
Q3-99999-99999
95-th percentile0.9190.909
Maximum1.3941.394
Range100000.39100000.39
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation36501.88336152.558
Coefficient of variation (CV)-0.43366848-0.42763692
Kurtosis1.50607011.65293
Mean-84170.015-84540.311
Median Absolute Deviation (MAD)00
Skewness1.87242041.9111889
Sum-2.2625742 × 109-9.7398892 × 108
Variance1.3323874 × 1091.3070074 × 109
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:36.065688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9999922626
84.2%
1407
 
1.5%
0107
 
0.4%
0.94939
 
0.1%
0.96729
 
0.1%
0.84228
 
0.1%
0.93124
 
0.1%
0.89424
 
0.1%
0.92922
 
0.1%
0.84322
 
0.1%
Other values (733)3553
 
13.2%
ValueCountFrequency (%)
-999999740
84.5%
1149
 
1.3%
046
 
0.4%
0.98417
 
0.1%
0.93113
 
0.1%
0.84313
 
0.1%
0.9712
 
0.1%
0.76611
 
0.1%
0.84211
 
0.1%
0.85611
 
0.1%
Other values (561)1498
 
13.0%
ValueCountFrequency (%)
-9999922626
84.2%
0107
 
0.4%
0.0041
 
< 0.1%
0.0062
 
< 0.1%
0.0073
 
< 0.1%
0.0082
 
< 0.1%
0.013
 
< 0.1%
0.0121
 
< 0.1%
0.0262
 
< 0.1%
0.0281
 
< 0.1%
ValueCountFrequency (%)
-999999740
84.5%
046
 
0.4%
0.0041
 
< 0.1%
0.0071
 
< 0.1%
0.0131
 
< 0.1%
0.0281
 
< 0.1%
0.0321
 
< 0.1%
0.041
 
< 0.1%
0.0432
 
< 0.1%
0.0442
 
< 0.1%
ValueCountFrequency (%)
-999999740
36.2%
046
 
0.2%
0.0041
 
< 0.1%
0.0071
 
< 0.1%
0.0131
 
< 0.1%
0.0281
 
< 0.1%
0.0321
 
< 0.1%
0.041
 
< 0.1%
0.0432
 
< 0.1%
0.0442
 
< 0.1%
ValueCountFrequency (%)
-9999922626
196.4%
0107
 
0.9%
0.0041
 
< 0.1%
0.0062
 
< 0.1%
0.0073
 
< 0.1%
0.0082
 
< 0.1%
0.013
 
< 0.1%
0.0121
 
< 0.1%
0.0262
 
< 0.1%
0.0281
 
< 0.1%

PL_Flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
21662 
1
5219 
0
9343 
1
2178 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row10
2nd row00
3rd row00
4th row00
5th row10

Common Values

ValueCountFrequency (%)
021662
80.6%
15219
 
19.4%
ValueCountFrequency (%)
09343
81.1%
12178
 
18.9%

Length

2026-01-17T22:57:36.144018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:36.184619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:36.211754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
021662
80.6%
15219
 
19.4%
ValueCountFrequency (%)
09343
81.1%
12178
 
18.9%

Most occurring characters

ValueCountFrequency (%)
021662
80.6%
15219
 
19.4%
ValueCountFrequency (%)
09343
81.1%
12178
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
021662
80.6%
15219
 
19.4%
ValueCountFrequency (%)
09343
81.1%
12178
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
021662
80.6%
15219
 
19.4%
ValueCountFrequency (%)
09343
81.1%
12178
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
021662
80.6%
15219
 
19.4%
ValueCountFrequency (%)
09343
81.1%
12178
 
18.9%

pct_PL_enq_L6m_of_L12m
Real number (ℝ)

 TrainTest
Distinct7355
Distinct (%)0.3%0.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.203087870.20636065
 TrainTest
Minimum00
Maximum11
Zeros205458785
Zeros (%)76.4%76.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:36.280467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.382126870.3856842
Coefficient of variation (CV)1.88158391.8689813
Kurtosis0.272594860.19768033
Mean0.203087870.20636065
Median Absolute Deviation (MAD)00
Skewness1.45856251.4361805
Sum5459.2052377.481
Variance0.146020940.1487523
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:36.380230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020545
76.4%
14363
 
16.2%
0.5655
 
2.4%
0.667288
 
1.1%
0.333217
 
0.8%
0.75132
 
0.5%
0.870
 
0.3%
0.470
 
0.3%
0.2561
 
0.2%
0.85755
 
0.2%
Other values (63)425
 
1.6%
ValueCountFrequency (%)
08785
76.3%
11937
 
16.8%
0.5284
 
2.5%
0.667135
 
1.2%
0.33382
 
0.7%
0.7547
 
0.4%
0.824
 
0.2%
0.2523
 
0.2%
0.83322
 
0.2%
0.421
 
0.2%
Other values (45)161
 
1.4%
ValueCountFrequency (%)
020545
76.4%
0.11
 
< 0.1%
0.1253
 
< 0.1%
0.1332
 
< 0.1%
0.1436
 
< 0.1%
0.16715
 
0.1%
0.232
 
0.1%
0.2221
 
< 0.1%
0.2561
 
0.2%
0.2732
 
< 0.1%
ValueCountFrequency (%)
08785
76.3%
0.1111
 
< 0.1%
0.1251
 
< 0.1%
0.1437
 
0.1%
0.1672
 
< 0.1%
0.1821
 
< 0.1%
0.214
 
0.1%
0.2223
 
< 0.1%
0.2523
 
0.2%
0.2731
 
< 0.1%
ValueCountFrequency (%)
08785
32.7%
0.1111
 
< 0.1%
0.1251
 
< 0.1%
0.1437
 
< 0.1%
0.1672
 
< 0.1%
0.1821
 
< 0.1%
0.214
 
0.1%
0.2223
 
< 0.1%
0.2523
 
0.1%
0.2731
 
< 0.1%
ValueCountFrequency (%)
020545
178.3%
0.11
 
< 0.1%
0.1253
 
< 0.1%
0.1332
 
< 0.1%
0.1436
 
0.1%
0.16715
 
0.1%
0.232
 
0.3%
0.2221
 
< 0.1%
0.2561
 
0.5%
0.2732
 
< 0.1%

pct_CC_enq_L6m_of_L12m
Real number (ℝ)

 TrainTest
Distinct4941
Distinct (%)0.2%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0966273580.097288169
 TrainTest
Minimum00
Maximum11
Zeros2368910141
Zeros (%)88.1%88.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.277801930.2787292
Coefficient of variation (CV)2.87498212.8649856
Kurtosis5.66729615.6115859
Mean0.0966273580.097288169
Median Absolute Deviation (MAD)00
Skewness2.70875312.698812
Sum2597.441120.857
Variance0.077173910.077689968
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
023689
88.1%
11962
 
7.3%
0.5403
 
1.5%
0.667158
 
0.6%
0.333157
 
0.6%
0.7576
 
0.3%
0.2573
 
0.3%
0.645
 
0.2%
0.443
 
0.2%
0.829
 
0.1%
Other values (39)246
 
0.9%
ValueCountFrequency (%)
010141
88.0%
1852
 
7.4%
0.5184
 
1.6%
0.66774
 
0.6%
0.33372
 
0.6%
0.7531
 
0.3%
0.2526
 
0.2%
0.619
 
0.2%
0.216
 
0.1%
0.815
 
0.1%
Other values (31)91
 
0.8%
ValueCountFrequency (%)
023689
88.1%
0.0912
 
< 0.1%
0.1251
 
< 0.1%
0.1435
 
< 0.1%
0.1673
 
< 0.1%
0.222
 
0.1%
0.2227
 
< 0.1%
0.2573
 
0.3%
0.2733
 
< 0.1%
0.28611
 
< 0.1%
ValueCountFrequency (%)
010141
88.0%
0.11
 
< 0.1%
0.1676
 
0.1%
0.216
 
0.1%
0.2224
 
< 0.1%
0.2526
 
0.2%
0.2731
 
< 0.1%
0.2863
 
< 0.1%
0.33
 
< 0.1%
0.33372
 
0.6%
ValueCountFrequency (%)
010141
37.7%
0.11
 
< 0.1%
0.1676
 
< 0.1%
0.216
 
0.1%
0.2224
 
< 0.1%
0.2526
 
0.1%
0.2731
 
< 0.1%
0.2863
 
< 0.1%
0.33
 
< 0.1%
0.33372
 
0.3%
ValueCountFrequency (%)
023689
205.6%
0.0912
 
< 0.1%
0.1251
 
< 0.1%
0.1435
 
< 0.1%
0.1673
 
< 0.1%
0.222
 
0.2%
0.2227
 
0.1%
0.2573
 
0.6%
0.2733
 
< 0.1%
0.28611
 
0.1%

pct_PL_enq_L6m_of_ever
Real number (ℝ)

 TrainTest
Distinct11178
Distinct (%)0.4%0.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.17596760.17989445
 TrainTest
Minimum00
Maximum11
Zeros205458785
Zeros (%)76.4%76.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:36.801302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.349040740.35330272
Coefficient of variation (CV)1.98355121.9639445
Kurtosis1.15644381.0252407
Mean0.17596760.17989445
Median Absolute Deviation (MAD)00
Skewness1.69582121.6611268
Sum4730.1852072.564
Variance0.121829430.12482281
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:36.903092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020545
76.4%
13357
 
12.5%
0.5826
 
3.1%
0.333389
 
1.4%
0.667282
 
1.0%
0.25218
 
0.8%
0.75149
 
0.6%
0.4108
 
0.4%
0.2101
 
0.4%
0.16782
 
0.3%
Other values (101)824
 
3.1%
ValueCountFrequency (%)
08785
76.3%
11498
 
13.0%
0.5368
 
3.2%
0.333164
 
1.4%
0.667145
 
1.3%
0.2590
 
0.8%
0.7553
 
0.5%
0.252
 
0.5%
0.440
 
0.3%
0.628
 
0.2%
Other values (68)298
 
2.6%
ValueCountFrequency (%)
020545
76.4%
0.0451
 
< 0.1%
0.054
 
< 0.1%
0.0678
 
< 0.1%
0.07111
 
< 0.1%
0.0742
 
< 0.1%
0.0772
 
< 0.1%
0.08313
 
< 0.1%
0.0873
 
< 0.1%
0.0917
 
< 0.1%
ValueCountFrequency (%)
08785
76.3%
0.0452
 
< 0.1%
0.0571
 
< 0.1%
0.0711
 
< 0.1%
0.0771
 
< 0.1%
0.0834
 
< 0.1%
0.0912
 
< 0.1%
0.18
 
0.1%
0.1051
 
< 0.1%
0.11115
 
0.1%
ValueCountFrequency (%)
08785
32.7%
0.0452
 
< 0.1%
0.0571
 
< 0.1%
0.0711
 
< 0.1%
0.0771
 
< 0.1%
0.0834
 
< 0.1%
0.0912
 
< 0.1%
0.18
 
< 0.1%
0.1051
 
< 0.1%
0.11115
 
0.1%
ValueCountFrequency (%)
020545
178.3%
0.0451
 
< 0.1%
0.054
 
< 0.1%
0.0678
 
0.1%
0.07111
 
0.1%
0.0742
 
< 0.1%
0.0772
 
< 0.1%
0.08313
 
0.1%
0.0873
 
< 0.1%
0.0917
 
0.1%

pct_CC_enq_L6m_of_ever
Real number (ℝ)

 TrainTest
Distinct7867
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.07950080.079739606
 TrainTest
Minimum00
Maximum11
Zeros2368910141
Zeros (%)88.1%88.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:37.005024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.243935590.24416181
Coefficient of variation (CV)3.06834143.0619892
Kurtosis8.35824438.333079
Mean0.07950080.079739606
Median Absolute Deviation (MAD)00
Skewness3.11211513.1077971
Sum2137.061918.68
Variance0.0595045740.05961499
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:37.105268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023689
88.1%
11401
 
5.2%
0.5453
 
1.7%
0.333232
 
0.9%
0.25154
 
0.6%
0.667140
 
0.5%
0.295
 
0.4%
0.477
 
0.3%
0.7564
 
0.2%
0.16763
 
0.2%
Other values (68)513
 
1.9%
ValueCountFrequency (%)
010141
88.0%
1602
 
5.2%
0.5201
 
1.7%
0.33396
 
0.8%
0.2569
 
0.6%
0.66764
 
0.6%
0.243
 
0.4%
0.437
 
0.3%
0.16728
 
0.2%
0.7523
 
0.2%
Other values (57)217
 
1.9%
ValueCountFrequency (%)
023689
88.1%
0.0482
 
< 0.1%
0.052
 
< 0.1%
0.0513
 
< 0.1%
0.0591
 
< 0.1%
0.0621
 
< 0.1%
0.0673
 
< 0.1%
0.0692
 
< 0.1%
0.0718
 
< 0.1%
0.0771
 
< 0.1%
ValueCountFrequency (%)
010141
88.0%
0.0531
 
< 0.1%
0.0593
 
< 0.1%
0.0624
 
< 0.1%
0.0712
 
< 0.1%
0.0773
 
< 0.1%
0.0834
 
< 0.1%
0.0862
 
< 0.1%
0.09112
 
0.1%
0.15
 
< 0.1%
ValueCountFrequency (%)
010141
37.7%
0.0531
 
< 0.1%
0.0593
 
< 0.1%
0.0624
 
< 0.1%
0.0712
 
< 0.1%
0.0773
 
< 0.1%
0.0834
 
< 0.1%
0.0862
 
< 0.1%
0.09112
 
< 0.1%
0.15
 
< 0.1%
ValueCountFrequency (%)
023689
205.6%
0.0482
 
< 0.1%
0.052
 
< 0.1%
0.0513
 
< 0.1%
0.0591
 
< 0.1%
0.0621
 
< 0.1%
0.0673
 
< 0.1%
0.0692
 
< 0.1%
0.0718
 
0.1%
0.0771
 
< 0.1%

max_unsec_exposure_inPct
Real number (ℝ)

 TrainTest
Distinct57833712
Distinct (%)21.5%32.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-40617.968-40358.748
 TrainTest
Minimum-99999-99999
Maximum83672.142656.6
Zeros275112
Zeros (%)1.0%1.0%
Negative109294651
Negative (%)40.7%40.4%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:37.201358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile-99999-99999
Q1-99999-99999
median0.5670.572
Q32.8572.826
95-th percentile15.93115.217
Maximum83672.142656.6
Range183671.1142655.6
Interquartile range (IQR)100001.86100001.83

Descriptive statistics

 TrainTest
Standard deviation49172.54349076.033
Coefficient of variation (CV)-1.2106106-1.2159949
Kurtosis-1.8476999-1.8458029
Mean-40617.968-40358.748
Median Absolute Deviation (MAD)9.4338.86
Skewness-0.37659514-0.3923224
Sum-1.0918516 × 109-4.6497314 × 108
Variance2.417939 × 1092.408457 × 109
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:37.303488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9999910929
40.7%
0275
 
1.0%
175
 
0.3%
560
 
0.2%
0.00156
 
0.2%
1053
 
0.2%
253
 
0.2%
2.552
 
0.2%
442
 
0.2%
6.66741
 
0.2%
Other values (5773)15245
56.7%
ValueCountFrequency (%)
-999994651
40.4%
0112
 
1.0%
0.00140
 
0.3%
1037
 
0.3%
130
 
0.3%
526
 
0.2%
3.33323
 
0.2%
222
 
0.2%
2.521
 
0.2%
0.621
 
0.2%
Other values (3702)6538
56.7%
ValueCountFrequency (%)
-9999910929
40.7%
0275
 
1.0%
0.00156
 
0.2%
0.00225
 
0.1%
0.00313
 
< 0.1%
0.00413
 
< 0.1%
0.0053
 
< 0.1%
0.00614
 
0.1%
0.0076
 
< 0.1%
0.00810
 
< 0.1%
ValueCountFrequency (%)
-999994651
40.4%
0112
 
1.0%
0.00140
 
0.3%
0.00214
 
0.1%
0.0036
 
0.1%
0.0045
 
< 0.1%
0.0052
 
< 0.1%
0.0066
 
0.1%
0.0074
 
< 0.1%
0.0083
 
< 0.1%
ValueCountFrequency (%)
-999994651
17.3%
0112
 
0.4%
0.00140
 
0.1%
0.00214
 
0.1%
0.0036
 
< 0.1%
0.0045
 
< 0.1%
0.0052
 
< 0.1%
0.0066
 
< 0.1%
0.0074
 
< 0.1%
0.0083
 
< 0.1%
ValueCountFrequency (%)
-9999910929
94.9%
0275
 
2.4%
0.00156
 
0.5%
0.00225
 
0.2%
0.00313
 
0.1%
0.00413
 
0.1%
0.0053
 
< 0.1%
0.00614
 
0.1%
0.0076
 
0.1%
0.00810
 
0.1%

HL_Flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
19461 
1
7420 
0
8398 
1
3123 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
019461
72.4%
17420
 
27.6%
ValueCountFrequency (%)
08398
72.9%
13123
 
27.1%

Length

2026-01-17T22:57:37.385739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:37.425881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:37.452037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
019461
72.4%
17420
 
27.6%
ValueCountFrequency (%)
08398
72.9%
13123
 
27.1%

Most occurring characters

ValueCountFrequency (%)
019461
72.4%
17420
 
27.6%
ValueCountFrequency (%)
08398
72.9%
13123
 
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019461
72.4%
17420
 
27.6%
ValueCountFrequency (%)
08398
72.9%
13123
 
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019461
72.4%
17420
 
27.6%
ValueCountFrequency (%)
08398
72.9%
13123
 
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019461
72.4%
17420
 
27.6%
ValueCountFrequency (%)
08398
72.9%
13123
 
27.1%

GL_Flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
25377 
1
 
1504
0
10863 
1
 
658

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row00
2nd row00
3rd row01
4th row00
5th row00

Common Values

ValueCountFrequency (%)
025377
94.4%
11504
 
5.6%
ValueCountFrequency (%)
010863
94.3%
1658
 
5.7%

Length

2026-01-17T22:57:37.500610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:37.539688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:37.562243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
025377
94.4%
11504
 
5.6%
ValueCountFrequency (%)
010863
94.3%
1658
 
5.7%

Most occurring characters

ValueCountFrequency (%)
025377
94.4%
11504
 
5.6%
ValueCountFrequency (%)
010863
94.3%
1658
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
025377
94.4%
11504
 
5.6%
ValueCountFrequency (%)
010863
94.3%
1658
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
025377
94.4%
11504
 
5.6%
ValueCountFrequency (%)
010863
94.3%
1658
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26881
100.0%
ValueCountFrequency (%)
(unknown)11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
025377
94.4%
11504
 
5.6%
ValueCountFrequency (%)
010863
94.3%
1658
 
5.7%

last_prod_enq2
Categorical

 TrainTest
Distinct66
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
others
10499 
ConsumerLoan
9059 
PL
4211 
CC
1948 
AL
 
768
others
4369 
ConsumerLoan
3962 
PL
1812 
CC
867 
AL
 
329

Length

 TrainTest
Max length1212
Median length66
Mean length6.93233146.9558198
Min length22

Characters and Unicode

 TrainTest
Total characters18634880138
Distinct characters1515
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowPLothers
2nd rowConsumerLoanConsumerLoan
3rd rowothersConsumerLoan
4th rowConsumerLoanothers
5th rowConsumerLoanothers

Common Values

ValueCountFrequency (%)
others10499
39.1%
ConsumerLoan9059
33.7%
PL4211
15.7%
CC1948
 
7.2%
AL768
 
2.9%
HL396
 
1.5%
ValueCountFrequency (%)
others4369
37.9%
ConsumerLoan3962
34.4%
PL1812
15.7%
CC867
 
7.5%
AL329
 
2.9%
HL182
 
1.6%

Length

2026-01-17T22:57:37.606308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:37.654891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:37.712985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
others10499
39.1%
consumerloan9059
33.7%
pl4211
15.7%
cc1948
 
7.2%
al768
 
2.9%
hl396
 
1.5%
ValueCountFrequency (%)
others4369
37.9%
consumerloan3962
34.4%
pl1812
15.7%
cc867
 
7.5%
al329
 
2.9%
hl182
 
1.6%

Most occurring characters

ValueCountFrequency (%)
o28617
15.4%
r19558
10.5%
e19558
10.5%
s19558
10.5%
n18118
9.7%
L14434
7.7%
C12955
7.0%
t10499
 
5.6%
h10499
 
5.6%
u9059
 
4.9%
Other values (5)23493
12.6%
ValueCountFrequency (%)
o12293
15.3%
r8331
10.4%
e8331
10.4%
s8331
10.4%
n7924
9.9%
L6285
7.8%
C5696
7.1%
t4369
 
5.5%
h4369
 
5.5%
u3962
 
4.9%
Other values (5)10247
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)186348
100.0%
ValueCountFrequency (%)
(unknown)80138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o28617
15.4%
r19558
10.5%
e19558
10.5%
s19558
10.5%
n18118
9.7%
L14434
7.7%
C12955
7.0%
t10499
 
5.6%
h10499
 
5.6%
u9059
 
4.9%
Other values (5)23493
12.6%
ValueCountFrequency (%)
o12293
15.3%
r8331
10.4%
e8331
10.4%
s8331
10.4%
n7924
9.9%
L6285
7.8%
C5696
7.1%
t4369
 
5.5%
h4369
 
5.5%
u3962
 
4.9%
Other values (5)10247
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)186348
100.0%
ValueCountFrequency (%)
(unknown)80138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o28617
15.4%
r19558
10.5%
e19558
10.5%
s19558
10.5%
n18118
9.7%
L14434
7.7%
C12955
7.0%
t10499
 
5.6%
h10499
 
5.6%
u9059
 
4.9%
Other values (5)23493
12.6%
ValueCountFrequency (%)
o12293
15.3%
r8331
10.4%
e8331
10.4%
s8331
10.4%
n7924
9.9%
L6285
7.8%
C5696
7.1%
t4369
 
5.5%
h4369
 
5.5%
u3962
 
4.9%
Other values (5)10247
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)186348
100.0%
ValueCountFrequency (%)
(unknown)80138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o28617
15.4%
r19558
10.5%
e19558
10.5%
s19558
10.5%
n18118
9.7%
L14434
7.7%
C12955
7.0%
t10499
 
5.6%
h10499
 
5.6%
u9059
 
4.9%
Other values (5)23493
12.6%
ValueCountFrequency (%)
o12293
15.3%
r8331
10.4%
e8331
10.4%
s8331
10.4%
n7924
9.9%
L6285
7.8%
C5696
7.1%
t4369
 
5.5%
h4369
 
5.5%
u3962
 
4.9%
Other values (5)10247
12.8%

first_prod_enq2
Categorical

 TrainTest
Distinct66
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
others
14117 
ConsumerLoan
5994 
PL
2599 
CC
1902 
AL
1577 
others
5988 
ConsumerLoan
2602 
PL
1162 
CC
792 
AL
645 

Length

 TrainTest
Max length1212
Median length66
Mean length6.33049376.3374707
Min length22

Characters and Unicode

 TrainTest
Total characters17017073014
Distinct characters1515
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowothersothers
2nd rowothersConsumerLoan
3rd rowothersCC
4th rowothersothers
5th rowConsumerLoanothers

Common Values

ValueCountFrequency (%)
others14117
52.5%
ConsumerLoan5994
22.3%
PL2599
 
9.7%
CC1902
 
7.1%
AL1577
 
5.9%
HL692
 
2.6%
ValueCountFrequency (%)
others5988
52.0%
ConsumerLoan2602
22.6%
PL1162
 
10.1%
CC792
 
6.9%
AL645
 
5.6%
HL332
 
2.9%

Length

2026-01-17T22:57:37.784550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2026-01-17T22:57:37.846685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:37.899787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
others14117
52.5%
consumerloan5994
22.3%
pl2599
 
9.7%
cc1902
 
7.1%
al1577
 
5.9%
hl692
 
2.6%
ValueCountFrequency (%)
others5988
52.0%
consumerloan2602
22.6%
pl1162
 
10.1%
cc792
 
6.9%
al645
 
5.6%
hl332
 
2.9%

Most occurring characters

ValueCountFrequency (%)
o26105
15.3%
r20111
11.8%
e20111
11.8%
s20111
11.8%
t14117
8.3%
h14117
8.3%
n11988
7.0%
L10862
6.4%
C9798
 
5.8%
u5994
 
3.5%
Other values (5)16856
9.9%
ValueCountFrequency (%)
o11192
15.3%
r8590
11.8%
e8590
11.8%
s8590
11.8%
t5988
8.2%
h5988
8.2%
n5204
7.1%
L4741
6.5%
C4186
 
5.7%
u2602
 
3.6%
Other values (5)7343
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)170170
100.0%
ValueCountFrequency (%)
(unknown)73014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o26105
15.3%
r20111
11.8%
e20111
11.8%
s20111
11.8%
t14117
8.3%
h14117
8.3%
n11988
7.0%
L10862
6.4%
C9798
 
5.8%
u5994
 
3.5%
Other values (5)16856
9.9%
ValueCountFrequency (%)
o11192
15.3%
r8590
11.8%
e8590
11.8%
s8590
11.8%
t5988
8.2%
h5988
8.2%
n5204
7.1%
L4741
6.5%
C4186
 
5.7%
u2602
 
3.6%
Other values (5)7343
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)170170
100.0%
ValueCountFrequency (%)
(unknown)73014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o26105
15.3%
r20111
11.8%
e20111
11.8%
s20111
11.8%
t14117
8.3%
h14117
8.3%
n11988
7.0%
L10862
6.4%
C9798
 
5.8%
u5994
 
3.5%
Other values (5)16856
9.9%
ValueCountFrequency (%)
o11192
15.3%
r8590
11.8%
e8590
11.8%
s8590
11.8%
t5988
8.2%
h5988
8.2%
n5204
7.1%
L4741
6.5%
C4186
 
5.7%
u2602
 
3.6%
Other values (5)7343
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)170170
100.0%
ValueCountFrequency (%)
(unknown)73014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o26105
15.3%
r20111
11.8%
e20111
11.8%
s20111
11.8%
t14117
8.3%
h14117
8.3%
n11988
7.0%
L10862
6.4%
C9798
 
5.8%
u5994
 
3.5%
Other values (5)16856
9.9%
ValueCountFrequency (%)
o11192
15.3%
r8590
11.8%
e8590
11.8%
s8590
11.8%
t5988
8.2%
h5988
8.2%
n5204
7.1%
L4741
6.5%
C4186
 
5.7%
u2602
 
3.6%
Other values (5)7343
10.1%

Credit_Score
Real number (ℝ)

 TrainTest
Distinct217191
Distinct (%)0.8%1.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean679.5146679.74837
 TrainTest
Minimum469509
Maximum811811
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:37.987034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum469509
5-th percentile648648
Q1668668
median679679
Q3690691
95-th percentile714714
Maximum811811
Range342302
Interquartile range (IQR)2223

Descriptive statistics

 TrainTest
Standard deviation21.21264420.815079
Coefficient of variation (CV)0.0312173490.030621741
Kurtosis4.92421543.7683018
Mean679.5146679.74837
Median Absolute Deviation (MAD)1111
Skewness-0.35973311-0.12074858
Sum182660327831381
Variance449.97628433.26749
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:38.088861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
679734
 
2.7%
682727
 
2.7%
678700
 
2.6%
672660
 
2.5%
680653
 
2.4%
681637
 
2.4%
684636
 
2.4%
673624
 
2.3%
683618
 
2.3%
686612
 
2.3%
Other values (207)20280
75.4%
ValueCountFrequency (%)
679319
 
2.8%
682311
 
2.7%
680300
 
2.6%
678296
 
2.6%
681284
 
2.5%
674282
 
2.4%
676278
 
2.4%
672273
 
2.4%
683267
 
2.3%
687258
 
2.2%
Other values (181)8653
75.1%
ValueCountFrequency (%)
4692
< 0.1%
4991
 
< 0.1%
5092
< 0.1%
5111
 
< 0.1%
5131
 
< 0.1%
5201
 
< 0.1%
5291
 
< 0.1%
5312
< 0.1%
5383
< 0.1%
5391
 
< 0.1%
ValueCountFrequency (%)
5091
< 0.1%
5131
< 0.1%
5201
< 0.1%
5361
< 0.1%
5381
< 0.1%
5462
< 0.1%
5481
< 0.1%
5531
< 0.1%
5681
< 0.1%
5811
< 0.1%
ValueCountFrequency (%)
5091
< 0.1%
5131
< 0.1%
5201
< 0.1%
5361
< 0.1%
5381
< 0.1%
5462
< 0.1%
5481
< 0.1%
5531
< 0.1%
5681
< 0.1%
5811
< 0.1%
ValueCountFrequency (%)
4692
< 0.1%
4991
 
< 0.1%
5092
< 0.1%
5111
 
< 0.1%
5131
 
< 0.1%
5201
 
< 0.1%
5291
 
< 0.1%
5312
< 0.1%
5383
< 0.1%
5391
 
< 0.1%

Total_TL
Real number (ℝ)

 TrainTest
Distinct8475
Distinct (%)0.3%0.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean5.32405795.2435552
 TrainTest
Minimum11
Maximum235235
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:38.188557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum11
5-th percentile11
Q111
median33
Q366
95-th percentile1818
Maximum235235
Range234234
Interquartile range (IQR)55

Descriptive statistics

 TrainTest
Standard deviation7.62006127.652674
Coefficient of variation (CV)1.43125061.4594438
Kurtosis67.935641104.97264
Mean5.32405795.2435552
Median Absolute Deviation (MAD)22
Skewness5.51861476.6207785
Sum14311660411
Variance58.06533258.563419
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:38.286360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17984
29.7%
24647
17.3%
33156
 
11.7%
42072
 
7.7%
51500
 
5.6%
61217
 
4.5%
7921
 
3.4%
8753
 
2.8%
9637
 
2.4%
10475
 
1.8%
Other values (74)3519
13.1%
ValueCountFrequency (%)
13489
30.3%
21974
17.1%
31286
 
11.2%
4893
 
7.8%
5697
 
6.0%
6545
 
4.7%
7391
 
3.4%
8309
 
2.7%
9291
 
2.5%
10205
 
1.8%
Other values (65)1441
12.5%
ValueCountFrequency (%)
17984
29.7%
24647
17.3%
33156
 
11.7%
42072
 
7.7%
51500
 
5.6%
61217
 
4.5%
7921
 
3.4%
8753
 
2.8%
9637
 
2.4%
10475
 
1.8%
ValueCountFrequency (%)
13489
30.3%
21974
17.1%
31286
 
11.2%
4893
 
7.8%
5697
 
6.0%
6545
 
4.7%
7391
 
3.4%
8309
 
2.7%
9291
 
2.5%
10205
 
1.8%
ValueCountFrequency (%)
13489
13.0%
21974
7.3%
31286
 
4.8%
4893
 
3.3%
5697
 
2.6%
6545
 
2.0%
7391
 
1.5%
8309
 
1.1%
9291
 
1.1%
10205
 
0.8%
ValueCountFrequency (%)
17984
69.3%
24647
40.3%
33156
 
27.4%
42072
 
18.0%
51500
 
13.0%
61217
 
10.6%
7921
 
8.0%
8753
 
6.5%
9637
 
5.5%
10475
 
4.1%

Tot_Closed_TL
Real number (ℝ)

 TrainTest
Distinct7364
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.96320822.923097
 TrainTest
Minimum00
Maximum216216
Zeros94404047
Zeros (%)35.1%35.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:38.382223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median11
Q333
95-th percentile1312
Maximum216216
Range216216
Interquartile range (IQR)33

Descriptive statistics

 TrainTest
Standard deviation6.10010316.2197732
Coefficient of variation (CV)2.05861442.1278026
Kurtosis115.58486169.1179
Mean2.96320822.923097
Median Absolute Deviation (MAD)11
Skewness7.24748888.6210426
Sum7965433677
Variance37.21125838.685578
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:38.478574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09440
35.1%
16536
24.3%
23035
 
11.3%
31785
 
6.6%
41186
 
4.4%
5937
 
3.5%
6669
 
2.5%
7532
 
2.0%
8364
 
1.4%
9335
 
1.2%
Other values (63)2062
 
7.7%
ValueCountFrequency (%)
04047
35.1%
12811
24.4%
21342
 
11.6%
3780
 
6.8%
4489
 
4.2%
5410
 
3.6%
6281
 
2.4%
7222
 
1.9%
8161
 
1.4%
9121
 
1.1%
Other values (54)857
 
7.4%
ValueCountFrequency (%)
09440
35.1%
16536
24.3%
23035
 
11.3%
31785
 
6.6%
41186
 
4.4%
5937
 
3.5%
6669
 
2.5%
7532
 
2.0%
8364
 
1.4%
9335
 
1.2%
ValueCountFrequency (%)
04047
35.1%
12811
24.4%
21342
 
11.6%
3780
 
6.8%
4489
 
4.2%
5410
 
3.6%
6281
 
2.4%
7222
 
1.9%
8161
 
1.4%
9121
 
1.1%
ValueCountFrequency (%)
04047
15.1%
12811
10.5%
21342
 
5.0%
3780
 
2.9%
4489
 
1.8%
5410
 
1.5%
6281
 
1.0%
7222
 
0.8%
8161
 
0.6%
9121
 
0.5%
ValueCountFrequency (%)
09440
81.9%
16536
56.7%
23035
 
26.3%
31785
 
15.5%
41186
 
10.3%
5937
 
8.1%
6669
 
5.8%
7532
 
4.6%
8364
 
3.2%
9335
 
2.9%

Tot_Active_TL
Real number (ℝ)

 TrainTest
Distinct2927
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.36084972.3204583
 TrainTest
Minimum00
Maximum4737
Zeros40771682
Zeros (%)15.2%14.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q111
median21
Q333
95-th percentile77
Maximum4737
Range4737
Interquartile range (IQR)22

Descriptive statistics

 TrainTest
Standard deviation2.60298132.5100643
Coefficient of variation (CV)1.10256121.0817106
Kurtosis15.72764312.786011
Mean2.36084972.3204583
Median Absolute Deviation (MAD)11
Skewness2.85918512.6975498
Sum6346226734
Variance6.77551186.3004226
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
19261
34.5%
25051
18.8%
04077
15.2%
32934
 
10.9%
41718
 
6.4%
51167
 
4.3%
6828
 
3.1%
7555
 
2.1%
8409
 
1.5%
9256
 
1.0%
Other values (19)625
 
2.3%
ValueCountFrequency (%)
14119
35.8%
22105
18.3%
01682
14.6%
31283
 
11.1%
4752
 
6.5%
5492
 
4.3%
6339
 
2.9%
7227
 
2.0%
8173
 
1.5%
998
 
0.9%
Other values (17)251
 
2.2%
ValueCountFrequency (%)
04077
15.2%
19261
34.5%
25051
18.8%
32934
 
10.9%
41718
 
6.4%
51167
 
4.3%
6828
 
3.1%
7555
 
2.1%
8409
 
1.5%
9256
 
1.0%
ValueCountFrequency (%)
01682
14.6%
14119
35.8%
22105
18.3%
31283
 
11.1%
4752
 
6.5%
5492
 
4.3%
6339
 
2.9%
7227
 
2.0%
8173
 
1.5%
998
 
0.9%
ValueCountFrequency (%)
01682
6.3%
14119
15.3%
22105
7.8%
31283
 
4.8%
4752
 
2.8%
5492
 
1.8%
6339
 
1.3%
7227
 
0.8%
8173
 
0.6%
998
 
0.4%
ValueCountFrequency (%)
04077
35.4%
19261
80.4%
25051
43.8%
32934
 
25.5%
41718
 
14.9%
51167
 
10.1%
6828
 
7.2%
7555
 
4.8%
8409
 
3.6%
9256
 
2.2%

Total_TL_opened_L6M
Real number (ℝ)

 TrainTest
Distinct1817
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.812730180.80331568
 TrainTest
Minimum00
Maximum1818
Zeros154216654
Zeros (%)57.4%57.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile33
Maximum1818
Range1818
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.39601481.3767134
Coefficient of variation (CV)1.71768541.7137887
Kurtosis16.94342316.052434
Mean0.812730180.80331568
Median Absolute Deviation (MAD)00
Skewness3.24212623.1816688
Sum218479255
Variance1.94885731.8953397
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
015421
57.4%
16666
24.8%
22368
 
8.8%
31140
 
4.2%
4556
 
2.1%
5306
 
1.1%
6138
 
0.5%
7128
 
0.5%
864
 
0.2%
926
 
0.1%
Other values (8)68
 
0.3%
ValueCountFrequency (%)
06654
57.8%
12786
24.2%
21084
 
9.4%
3460
 
4.0%
4228
 
2.0%
5127
 
1.1%
676
 
0.7%
748
 
0.4%
819
 
0.2%
1112
 
0.1%
Other values (7)27
 
0.2%
ValueCountFrequency (%)
015421
57.4%
16666
24.8%
22368
 
8.8%
31140
 
4.2%
4556
 
2.1%
5306
 
1.1%
6138
 
0.5%
7128
 
0.5%
864
 
0.2%
926
 
0.1%
ValueCountFrequency (%)
06654
57.8%
12786
24.2%
21084
 
9.4%
3460
 
4.0%
4228
 
2.0%
5127
 
1.1%
676
 
0.7%
748
 
0.4%
819
 
0.2%
99
 
0.1%
ValueCountFrequency (%)
06654
24.8%
12786
10.4%
21084
 
4.0%
3460
 
1.7%
4228
 
0.8%
5127
 
0.5%
676
 
0.3%
748
 
0.2%
819
 
0.1%
99
 
< 0.1%
ValueCountFrequency (%)
015421
133.9%
16666
57.9%
22368
 
20.6%
31140
 
9.9%
4556
 
4.8%
5306
 
2.7%
6138
 
1.2%
7128
 
1.1%
864
 
0.6%
926
 
0.2%

Tot_TL_closed_L6M
Real number (ℝ)

 TrainTest
Distinct1616
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.471745840.47200764
 TrainTest
Minimum00
Maximum1918
Zeros195938371
Zeros (%)72.9%72.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:38.551355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile22
Maximum1918
Range1918
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.07682551.0845835
Coefficient of variation (CV)2.28263922.2978092
Kurtosis29.63564732.578272
Mean0.471745840.47200764
Median Absolute Deviation (MAD)00
Skewness4.27860634.4947366
Sum126815438
Variance1.15955321.1763214
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:38.611221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
019593
72.9%
14715
 
17.5%
21352
 
5.0%
3542
 
2.0%
4311
 
1.2%
5165
 
0.6%
678
 
0.3%
741
 
0.2%
827
 
0.1%
926
 
0.1%
Other values (6)31
 
0.1%
ValueCountFrequency (%)
08371
72.7%
12048
 
17.8%
2617
 
5.4%
3211
 
1.8%
4109
 
0.9%
565
 
0.6%
645
 
0.4%
720
 
0.2%
812
 
0.1%
97
 
0.1%
Other values (6)16
 
0.1%
ValueCountFrequency (%)
019593
72.9%
14715
 
17.5%
21352
 
5.0%
3542
 
2.0%
4311
 
1.2%
5165
 
0.6%
678
 
0.3%
741
 
0.2%
827
 
0.1%
926
 
0.1%
ValueCountFrequency (%)
08371
72.7%
12048
 
17.8%
2617
 
5.4%
3211
 
1.8%
4109
 
0.9%
565
 
0.6%
645
 
0.4%
720
 
0.2%
812
 
0.1%
97
 
0.1%
ValueCountFrequency (%)
08371
31.1%
12048
 
7.6%
2617
 
2.3%
3211
 
0.8%
4109
 
0.4%
565
 
0.2%
645
 
0.2%
720
 
0.1%
812
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
019593
170.1%
14715
 
40.9%
21352
 
11.7%
3542
 
4.7%
4311
 
2.7%
5165
 
1.4%
678
 
0.7%
741
 
0.4%
827
 
0.2%
926
 
0.2%

pct_tl_open_L6M
Real number (ℝ)

 TrainTest
Distinct227188
Distinct (%)0.8%1.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.185712180.18446376
 TrainTest
Minimum00
Maximum11
Zeros154216654
Zeros (%)57.4%57.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:38.706363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q30.3120.324
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.3120.324

Descriptive statistics

 TrainTest
Standard deviation0.291792380.29184159
Coefficient of variation (CV)1.57120761.582108
Kurtosis1.66661261.7162148
Mean0.185712180.18446376
Median Absolute Deviation (MAD)00
Skewness1.63342981.6486066
Sum4992.1292125.207
Variance0.0851427950.085171512
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:38.820534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015421
57.4%
0.51908
 
7.1%
11795
 
6.7%
0.3331222
 
4.5%
0.25807
 
3.0%
0.2546
 
2.0%
0.667521
 
1.9%
0.167401
 
1.5%
0.143294
 
1.1%
0.4268
 
1.0%
Other values (217)3698
 
13.8%
ValueCountFrequency (%)
06654
57.8%
0.5828
 
7.2%
1777
 
6.7%
0.333520
 
4.5%
0.25297
 
2.6%
0.2242
 
2.1%
0.667198
 
1.7%
0.167176
 
1.5%
0.4142
 
1.2%
0.143136
 
1.2%
Other values (178)1551
 
13.5%
ValueCountFrequency (%)
015421
57.4%
0.014
 
< 0.1%
0.0131
 
< 0.1%
0.0151
 
< 0.1%
0.0162
 
< 0.1%
0.0173
 
< 0.1%
0.0193
 
< 0.1%
0.025
 
< 0.1%
0.0211
 
< 0.1%
0.0224
 
< 0.1%
ValueCountFrequency (%)
06654
57.8%
0.011
 
< 0.1%
0.0132
 
< 0.1%
0.0142
 
< 0.1%
0.0163
 
< 0.1%
0.021
 
< 0.1%
0.0221
 
< 0.1%
0.0232
 
< 0.1%
0.0243
 
< 0.1%
0.0252
 
< 0.1%
ValueCountFrequency (%)
06654
24.8%
0.011
 
< 0.1%
0.0132
 
< 0.1%
0.0142
 
< 0.1%
0.0163
 
< 0.1%
0.021
 
< 0.1%
0.0221
 
< 0.1%
0.0232
 
< 0.1%
0.0243
 
< 0.1%
0.0252
 
< 0.1%
ValueCountFrequency (%)
015421
133.9%
0.014
 
< 0.1%
0.0131
 
< 0.1%
0.0151
 
< 0.1%
0.0162
 
< 0.1%
0.0173
 
< 0.1%
0.0193
 
< 0.1%
0.025
 
< 0.1%
0.0211
 
< 0.1%
0.0224
 
< 0.1%

pct_tl_closed_L6M
Real number (ℝ)

 TrainTest
Distinct204159
Distinct (%)0.8%1.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0873529260.087087058
 TrainTest
Minimum00
Maximum11
Zeros195938371
Zeros (%)72.9%72.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:38.926413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q30.0710.077
95-th percentile0.50.5
Maximum11
Range11
Interquartile range (IQR)0.0710.077

Descriptive statistics

 TrainTest
Standard deviation0.199610840.19752418
Coefficient of variation (CV)2.28510772.2681232
Kurtosis9.76287279.8052527
Mean0.0873529260.087087058
Median Absolute Deviation (MAD)00
Skewness3.03819053.0319629
Sum2348.1341003.33
Variance0.0398444890.0390158
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:39.028296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019593
72.9%
0.5837
 
3.1%
0.333752
 
2.8%
1701
 
2.6%
0.25641
 
2.4%
0.2492
 
1.8%
0.167418
 
1.6%
0.143322
 
1.2%
0.125247
 
0.9%
0.111237
 
0.9%
Other values (194)2641
 
9.8%
ValueCountFrequency (%)
08371
72.7%
0.5367
 
3.2%
0.333306
 
2.7%
1287
 
2.5%
0.25270
 
2.3%
0.2261
 
2.3%
0.167190
 
1.6%
0.143151
 
1.3%
0.111115
 
1.0%
0.125114
 
1.0%
Other values (149)1089
 
9.5%
ValueCountFrequency (%)
019593
72.9%
0.0123
 
< 0.1%
0.0131
 
< 0.1%
0.0162
 
< 0.1%
0.0172
 
< 0.1%
0.0181
 
< 0.1%
0.0192
 
< 0.1%
0.025
 
< 0.1%
0.0218
 
< 0.1%
0.0222
 
< 0.1%
ValueCountFrequency (%)
08371
72.7%
0.0081
 
< 0.1%
0.011
 
< 0.1%
0.0132
 
< 0.1%
0.0162
 
< 0.1%
0.0171
 
< 0.1%
0.022
 
< 0.1%
0.0211
 
< 0.1%
0.0234
 
< 0.1%
0.0243
 
< 0.1%
ValueCountFrequency (%)
08371
31.1%
0.0081
 
< 0.1%
0.011
 
< 0.1%
0.0132
 
< 0.1%
0.0162
 
< 0.1%
0.0171
 
< 0.1%
0.022
 
< 0.1%
0.0211
 
< 0.1%
0.0234
 
< 0.1%
0.0243
 
< 0.1%
ValueCountFrequency (%)
019593
170.1%
0.0123
 
< 0.1%
0.0131
 
< 0.1%
0.0162
 
< 0.1%
0.0172
 
< 0.1%
0.0181
 
< 0.1%
0.0192
 
< 0.1%
0.025
 
< 0.1%
0.0218
 
0.1%
0.0222
 
< 0.1%

pct_active_tl
Real number (ℝ)

 TrainTest
Distinct332265
Distinct (%)1.2%2.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.58464190.58885383
 TrainTest
Minimum00
Maximum11
Zeros40771682
Zeros (%)15.2%14.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:39.127754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q10.2860.286
median0.5950.6
Q311
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.7140.714

Descriptive statistics

 TrainTest
Standard deviation0.372464410.369496
Coefficient of variation (CV)0.637081270.62748339
Kurtosis-1.3575591-1.3313392
Mean0.58464190.58885383
Median Absolute Deviation (MAD)0.4050.4
Skewness-0.24785968-0.26136069
Sum15715.7596784.185
Variance0.138729730.13652729
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:39.227210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19440
35.1%
04077
15.2%
0.52915
 
10.8%
0.6671440
 
5.4%
0.3331229
 
4.6%
0.75676
 
2.5%
0.25602
 
2.2%
0.6430
 
1.6%
0.4430
 
1.6%
0.2348
 
1.3%
Other values (322)5294
19.7%
ValueCountFrequency (%)
14047
35.1%
01682
14.6%
0.51318
 
11.4%
0.667601
 
5.2%
0.333503
 
4.4%
0.25279
 
2.4%
0.75278
 
2.4%
0.6223
 
1.9%
0.4198
 
1.7%
0.8151
 
1.3%
Other values (255)2241
19.5%
ValueCountFrequency (%)
04077
15.2%
0.0161
 
< 0.1%
0.022
 
< 0.1%
0.0212
 
< 0.1%
0.0221
 
< 0.1%
0.0252
 
< 0.1%
0.0261
 
< 0.1%
0.0271
 
< 0.1%
0.0293
 
< 0.1%
0.0314
 
< 0.1%
ValueCountFrequency (%)
01682
14.6%
0.0161
 
< 0.1%
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.021
 
< 0.1%
0.0211
 
< 0.1%
0.0241
 
< 0.1%
0.0251
 
< 0.1%
0.0281
 
< 0.1%
0.0291
 
< 0.1%
ValueCountFrequency (%)
01682
6.3%
0.0161
 
< 0.1%
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.021
 
< 0.1%
0.0211
 
< 0.1%
0.0241
 
< 0.1%
0.0251
 
< 0.1%
0.0281
 
< 0.1%
0.0291
 
< 0.1%
ValueCountFrequency (%)
04077
35.4%
0.0161
 
< 0.1%
0.022
 
< 0.1%
0.0212
 
< 0.1%
0.0221
 
< 0.1%
0.0252
 
< 0.1%
0.0261
 
< 0.1%
0.0271
 
< 0.1%
0.0293
 
< 0.1%
0.0314
 
< 0.1%

pct_closed_tl
Real number (ℝ)

 TrainTest
Distinct332265
Distinct (%)1.2%2.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.41535810.41114617
 TrainTest
Minimum00
Maximum11
Zeros94404047
Zeros (%)35.1%35.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:39.328976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median0.4050.4
Q30.7140.714
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.7140.714

Descriptive statistics

 TrainTest
Standard deviation0.372464410.369496
Coefficient of variation (CV)0.896730820.89869741
Kurtosis-1.3575591-1.3313392
Mean0.41535810.41114617
Median Absolute Deviation (MAD)0.4050.4
Skewness0.247859680.26136069
Sum11165.2414736.815
Variance0.138729730.13652729
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:39.430408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09440
35.1%
14077
15.2%
0.52915
 
10.8%
0.3331440
 
5.4%
0.6671229
 
4.6%
0.25676
 
2.5%
0.75602
 
2.2%
0.4430
 
1.6%
0.6430
 
1.6%
0.8348
 
1.3%
Other values (322)5294
19.7%
ValueCountFrequency (%)
04047
35.1%
11682
14.6%
0.51318
 
11.4%
0.333601
 
5.2%
0.667503
 
4.4%
0.75279
 
2.4%
0.25278
 
2.4%
0.4223
 
1.9%
0.6198
 
1.7%
0.2151
 
1.3%
Other values (255)2241
19.5%
ValueCountFrequency (%)
09440
35.1%
0.0531
 
< 0.1%
0.0592
 
< 0.1%
0.0671
 
< 0.1%
0.0713
 
< 0.1%
0.0773
 
< 0.1%
0.0835
 
< 0.1%
0.09110
 
< 0.1%
0.119
 
0.1%
0.11141
 
0.2%
ValueCountFrequency (%)
04047
35.1%
0.0621
 
< 0.1%
0.0671
 
< 0.1%
0.0772
 
< 0.1%
0.0834
 
< 0.1%
0.0914
 
< 0.1%
0.16
 
0.1%
0.11126
 
0.2%
0.1181
 
< 0.1%
0.12519
 
0.2%
ValueCountFrequency (%)
04047
15.1%
0.0621
 
< 0.1%
0.0671
 
< 0.1%
0.0772
 
< 0.1%
0.0834
 
< 0.1%
0.0914
 
< 0.1%
0.16
 
< 0.1%
0.11126
 
0.1%
0.1181
 
< 0.1%
0.12519
 
0.1%
ValueCountFrequency (%)
09440
81.9%
0.0531
 
< 0.1%
0.0592
 
< 0.1%
0.0671
 
< 0.1%
0.0713
 
< 0.1%
0.0773
 
< 0.1%
0.0835
 
< 0.1%
0.09110
 
0.1%
0.119
 
0.2%
0.11141
 
0.4%

Total_TL_opened_L12M
Real number (ℝ)

 TrainTest
Distinct3227
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.66225211.6467321
 TrainTest
Minimum00
Maximum3433
Zeros89563852
Zeros (%)33.3%33.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median11
Q322
95-th percentile66
Maximum3433
Range3433
Interquartile range (IQR)22

Descriptive statistics

 TrainTest
Standard deviation2.33919272.3150736
Coefficient of variation (CV)1.4072431.4058593
Kurtosis20.51170620.579484
Mean1.66225211.6467321
Median Absolute Deviation (MAD)11
Skewness3.44388253.4460603
Sum4468318972
Variance5.47182245.3595659
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
08956
33.3%
18459
31.5%
23964
14.7%
32003
 
7.5%
41166
 
4.3%
5711
 
2.6%
6486
 
1.8%
7365
 
1.4%
8189
 
0.7%
9142
 
0.5%
Other values (22)440
 
1.6%
ValueCountFrequency (%)
03852
33.4%
13638
31.6%
21703
14.8%
3835
 
7.2%
4513
 
4.5%
5316
 
2.7%
6191
 
1.7%
7149
 
1.3%
878
 
0.7%
971
 
0.6%
Other values (17)175
 
1.5%
ValueCountFrequency (%)
08956
33.3%
18459
31.5%
23964
14.7%
32003
 
7.5%
41166
 
4.3%
5711
 
2.6%
6486
 
1.8%
7365
 
1.4%
8189
 
0.7%
9142
 
0.5%
ValueCountFrequency (%)
03852
33.4%
13638
31.6%
21703
14.8%
3835
 
7.2%
4513
 
4.5%
5316
 
2.7%
6191
 
1.7%
7149
 
1.3%
878
 
0.7%
971
 
0.6%
ValueCountFrequency (%)
03852
14.3%
13638
13.5%
21703
6.3%
3835
 
3.1%
4513
 
1.9%
5316
 
1.2%
6191
 
0.7%
7149
 
0.6%
878
 
0.3%
971
 
0.3%
ValueCountFrequency (%)
08956
77.7%
18459
73.4%
23964
34.4%
32003
 
17.4%
41166
 
10.1%
5711
 
6.2%
6486
 
4.2%
7365
 
3.2%
8189
 
1.6%
9142
 
1.2%

Tot_TL_closed_L12M
Real number (ℝ)

 TrainTest
Distinct2321
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.800007440.79871539
 TrainTest
Minimum00
Maximum3327
Zeros163106996
Zeros (%)60.7%60.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile33
Maximum3327
Range3327
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.57000391.5378958
Coefficient of variation (CV)1.96248661.9254615
Kurtosis37.91861128.467992
Mean0.800007440.79871539
Median Absolute Deviation (MAD)00
Skewness4.4805874.0738376
Sum215059202
Variance2.46491222.3651233
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
016310
60.7%
16035
 
22.5%
22215
 
8.2%
3985
 
3.7%
4473
 
1.8%
5295
 
1.1%
6177
 
0.7%
7122
 
0.5%
882
 
0.3%
964
 
0.2%
Other values (13)123
 
0.5%
ValueCountFrequency (%)
06996
60.7%
12541
 
22.1%
2969
 
8.4%
3447
 
3.9%
4228
 
2.0%
590
 
0.8%
680
 
0.7%
758
 
0.5%
843
 
0.4%
924
 
0.2%
Other values (11)45
 
0.4%
ValueCountFrequency (%)
016310
60.7%
16035
 
22.5%
22215
 
8.2%
3985
 
3.7%
4473
 
1.8%
5295
 
1.1%
6177
 
0.7%
7122
 
0.5%
882
 
0.3%
964
 
0.2%
ValueCountFrequency (%)
06996
60.7%
12541
 
22.1%
2969
 
8.4%
3447
 
3.9%
4228
 
2.0%
590
 
0.8%
680
 
0.7%
758
 
0.5%
843
 
0.4%
924
 
0.2%
ValueCountFrequency (%)
06996
26.0%
12541
 
9.5%
2969
 
3.6%
3447
 
1.7%
4228
 
0.8%
590
 
0.3%
680
 
0.3%
758
 
0.2%
843
 
0.2%
924
 
0.1%
ValueCountFrequency (%)
016310
141.6%
16035
 
52.4%
22215
 
19.2%
3985
 
8.5%
4473
 
4.1%
5295
 
2.6%
6177
 
1.5%
7122
 
1.1%
882
 
0.7%
964
 
0.6%

pct_tl_open_L12M
Real number (ℝ)

 TrainTest
Distinct315241
Distinct (%)1.2%2.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.394914180.39510598
 TrainTest
Minimum00
Maximum11
Zeros89563852
Zeros (%)33.3%33.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:39.535164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median0.3330.333
Q30.70.714
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.70.714

Descriptive statistics

 TrainTest
Standard deviation0.385025680.38502784
Coefficient of variation (CV)0.97496040.97449257
Kurtosis-1.2701053-1.265714
Mean0.394914180.39510598
Median Absolute Deviation (MAD)0.3330.333
Skewness0.478682060.48136926
Sum10615.6884552.016
Variance0.148244780.14824644
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:39.637911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08956
33.3%
15596
20.8%
0.52659
 
9.9%
0.3331305
 
4.9%
0.667994
 
3.7%
0.25796
 
3.0%
0.2474
 
1.8%
0.4396
 
1.5%
0.75376
 
1.4%
0.167369
 
1.4%
Other values (305)4960
18.5%
ValueCountFrequency (%)
03852
33.4%
12408
20.9%
0.51148
 
10.0%
0.333585
 
5.1%
0.667400
 
3.5%
0.25337
 
2.9%
0.2227
 
2.0%
0.4197
 
1.7%
0.167165
 
1.4%
0.75152
 
1.3%
Other values (231)2050
17.8%
ValueCountFrequency (%)
08956
33.3%
0.0161
 
< 0.1%
0.0172
 
< 0.1%
0.0183
 
< 0.1%
0.0192
 
< 0.1%
0.024
 
< 0.1%
0.0216
 
< 0.1%
0.0221
 
< 0.1%
0.0234
 
< 0.1%
0.0253
 
< 0.1%
ValueCountFrequency (%)
03852
33.4%
0.0141
 
< 0.1%
0.0161
 
< 0.1%
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.022
 
< 0.1%
0.0213
 
< 0.1%
0.0221
 
< 0.1%
0.0232
 
< 0.1%
0.0241
 
< 0.1%
ValueCountFrequency (%)
03852
14.3%
0.0141
 
< 0.1%
0.0161
 
< 0.1%
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.022
 
< 0.1%
0.0213
 
< 0.1%
0.0221
 
< 0.1%
0.0232
 
< 0.1%
0.0241
 
< 0.1%
ValueCountFrequency (%)
08956
77.7%
0.0161
 
< 0.1%
0.0172
 
< 0.1%
0.0183
 
< 0.1%
0.0192
 
< 0.1%
0.024
 
< 0.1%
0.0216
 
0.1%
0.0221
 
< 0.1%
0.0234
 
< 0.1%
0.0253
 
< 0.1%

pct_tl_closed_L12M
Real number (ℝ)

 TrainTest
Distinct262204
Distinct (%)1.0%1.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.145302180.14503515
 TrainTest
Minimum00
Maximum11
Zeros163106996
Zeros (%)60.7%60.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:39.750010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q30.2220.231
95-th percentile0.6670.667
Maximum11
Range11
Interquartile range (IQR)0.2220.231

Descriptive statistics

 TrainTest
Standard deviation0.247493260.24504452
Coefficient of variation (CV)1.70330041.6895526
Kurtosis3.92934853.8741171
Mean0.145302180.14503515
Median Absolute Deviation (MAD)00
Skewness2.06649552.0415633
Sum3905.8681670.95
Variance0.0612529140.060046815
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:39.866342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016310
60.7%
0.51471
 
5.5%
0.3331246
 
4.6%
11166
 
4.3%
0.25943
 
3.5%
0.2629
 
2.3%
0.167537
 
2.0%
0.143391
 
1.5%
0.125279
 
1.0%
0.4270
 
1.0%
Other values (252)3639
 
13.5%
ValueCountFrequency (%)
06996
60.7%
0.5661
 
5.7%
0.333533
 
4.6%
1470
 
4.1%
0.25389
 
3.4%
0.2292
 
2.5%
0.167234
 
2.0%
0.143171
 
1.5%
0.4141
 
1.2%
0.667108
 
0.9%
Other values (194)1526
 
13.2%
ValueCountFrequency (%)
016310
60.7%
0.0081
 
< 0.1%
0.0091
 
< 0.1%
0.0131
 
< 0.1%
0.0161
 
< 0.1%
0.0183
 
< 0.1%
0.0192
 
< 0.1%
0.025
 
< 0.1%
0.0213
 
< 0.1%
0.0234
 
< 0.1%
ValueCountFrequency (%)
06996
60.7%
0.0081
 
< 0.1%
0.0091
 
< 0.1%
0.0141
 
< 0.1%
0.0162
 
< 0.1%
0.0172
 
< 0.1%
0.0181
 
< 0.1%
0.021
 
< 0.1%
0.0212
 
< 0.1%
0.0232
 
< 0.1%
ValueCountFrequency (%)
06996
26.0%
0.0081
 
< 0.1%
0.0091
 
< 0.1%
0.0141
 
< 0.1%
0.0162
 
< 0.1%
0.0172
 
< 0.1%
0.0181
 
< 0.1%
0.021
 
< 0.1%
0.0212
 
< 0.1%
0.0232
 
< 0.1%
ValueCountFrequency (%)
016310
141.6%
0.0081
 
< 0.1%
0.0091
 
< 0.1%
0.0131
 
< 0.1%
0.0161
 
< 0.1%
0.0183
 
< 0.1%
0.0192
 
< 0.1%
0.025
 
< 0.1%
0.0213
 
< 0.1%
0.0234
 
< 0.1%

Tot_Missed_Pmnt
Real number (ℝ)

 TrainTest
Distinct1916
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.57453220.57139137
 TrainTest
Minimum00
Maximum3434
Zeros175347529
Zeros (%)65.2%65.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile23
Maximum3434
Range3434
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.15184451.1232058
Coefficient of variation (CV)2.00483891.9657381
Kurtosis112.74475112.4823
Mean0.57453220.57139137
Median Absolute Deviation (MAD)00
Skewness6.49526656.2839758
Sum154446583
Variance1.32674571.2615912
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
017534
65.2%
16107
 
22.7%
21905
 
7.1%
3757
 
2.8%
4259
 
1.0%
5133
 
0.5%
679
 
0.3%
731
 
0.1%
825
 
0.1%
1013
 
< 0.1%
Other values (9)38
 
0.1%
ValueCountFrequency (%)
07529
65.4%
12594
 
22.5%
2805
 
7.0%
3343
 
3.0%
4115
 
1.0%
560
 
0.5%
635
 
0.3%
720
 
0.2%
86
 
0.1%
96
 
0.1%
Other values (6)8
 
0.1%
ValueCountFrequency (%)
017534
65.2%
16107
 
22.7%
21905
 
7.1%
3757
 
2.8%
4259
 
1.0%
5133
 
0.5%
679
 
0.3%
731
 
0.1%
825
 
0.1%
99
 
< 0.1%
ValueCountFrequency (%)
07529
65.4%
12594
 
22.5%
2805
 
7.0%
3343
 
3.0%
4115
 
1.0%
560
 
0.5%
635
 
0.3%
720
 
0.2%
86
 
0.1%
96
 
0.1%
ValueCountFrequency (%)
07529
28.0%
12594
 
9.6%
2805
 
3.0%
3343
 
1.3%
4115
 
0.4%
560
 
0.2%
635
 
0.1%
720
 
0.1%
86
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
017534
152.2%
16107
 
53.0%
21905
 
16.5%
3757
 
6.6%
4259
 
2.2%
5133
 
1.2%
679
 
0.7%
731
 
0.3%
825
 
0.2%
99
 
0.1%

Auto_TL
Real number (ℝ)

 TrainTest
Distinct1612
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.627990030.61262043
 TrainTest
Minimum00
Maximum2323
Zeros151356532
Zeros (%)56.3%56.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile22
Maximum2323
Range2323
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation0.963120170.92468674
Coefficient of variation (CV)1.53365521.5093959
Kurtosis38.44536940.570607
Mean0.627990030.61262043
Median Absolute Deviation (MAD)00
Skewness3.70651483.5477117
Sum168817058
Variance0.927600460.85504557
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
015135
56.3%
18553
31.8%
22126
 
7.9%
3635
 
2.4%
4246
 
0.9%
592
 
0.3%
645
 
0.2%
726
 
0.1%
127
 
< 0.1%
85
 
< 0.1%
Other values (6)11
 
< 0.1%
ValueCountFrequency (%)
06532
56.7%
13654
31.7%
2909
 
7.9%
3264
 
2.3%
495
 
0.8%
535
 
0.3%
618
 
0.2%
76
 
0.1%
84
 
< 0.1%
112
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
015135
56.3%
18553
31.8%
22126
 
7.9%
3635
 
2.4%
4246
 
0.9%
592
 
0.3%
645
 
0.2%
726
 
0.1%
85
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
06532
56.7%
13654
31.7%
2909
 
7.9%
3264
 
2.3%
495
 
0.8%
535
 
0.3%
618
 
0.2%
76
 
0.1%
84
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
06532
24.3%
13654
13.6%
2909
 
3.4%
3264
 
1.0%
495
 
0.4%
535
 
0.1%
618
 
0.1%
76
 
< 0.1%
84
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
015135
131.4%
18553
74.2%
22126
 
18.5%
3635
 
5.5%
4246
 
2.1%
592
 
0.8%
645
 
0.4%
726
 
0.2%
85
 
< 0.1%
91
 
< 0.1%

CC_TL
Real number (ℝ)

 TrainTest
Distinct1412
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.277073030.27367416
 TrainTest
Minimum00
Maximum1427
Zeros218799375
Zeros (%)81.4%81.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile21
Maximum1427
Range1427
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.75396670.75358549
Coefficient of variation (CV)2.7211842.7535866
Kurtosis46.878382158.40753
Mean0.277073030.27367416
Median Absolute Deviation (MAD)00
Skewness5.22506617.4603958
Sum74483153
Variance0.568465780.56789109
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
021879
81.4%
13655
 
13.6%
2821
 
3.1%
3265
 
1.0%
4142
 
0.5%
550
 
0.2%
621
 
0.1%
721
 
0.1%
811
 
< 0.1%
115
 
< 0.1%
Other values (4)11
 
< 0.1%
ValueCountFrequency (%)
09375
81.4%
11584
 
13.7%
2337
 
2.9%
3121
 
1.1%
459
 
0.5%
517
 
0.1%
614
 
0.1%
78
 
0.1%
82
 
< 0.1%
92
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
021879
81.4%
13655
 
13.6%
2821
 
3.1%
3265
 
1.0%
4142
 
0.5%
550
 
0.2%
621
 
0.1%
721
 
0.1%
811
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
09375
81.4%
11584
 
13.7%
2337
 
2.9%
3121
 
1.1%
459
 
0.5%
517
 
0.1%
614
 
0.1%
78
 
0.1%
82
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
09375
34.9%
11584
 
5.9%
2337
 
1.3%
3121
 
0.5%
459
 
0.2%
517
 
0.1%
614
 
0.1%
78
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
021879
189.9%
13655
 
31.7%
2821
 
7.1%
3265
 
2.3%
4142
 
1.2%
550
 
0.4%
621
 
0.2%
721
 
0.2%
811
 
0.1%
95
 
< 0.1%

Consumer_TL
Real number (ℝ)

 TrainTest
Distinct3733
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.31059111.2607413
 TrainTest
Minimum00
Maximum4139
Zeros143476189
Zeros (%)53.4%53.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile66
Maximum4139
Range4139
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation2.69034232.5302464
Coefficient of variation (CV)2.05277012.0069514
Kurtosis43.6833242.581997
Mean1.31059111.2607413
Median Absolute Deviation (MAD)00
Skewness5.21853755.1111997
Sum3523014525
Variance7.23794186.402147
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
014347
53.4%
15833
21.7%
22517
 
9.4%
31426
 
5.3%
4803
 
3.0%
5493
 
1.8%
6402
 
1.5%
7253
 
0.9%
8177
 
0.7%
9150
 
0.6%
Other values (27)480
 
1.8%
ValueCountFrequency (%)
06189
53.7%
12479
21.5%
21087
 
9.4%
3615
 
5.3%
4349
 
3.0%
5222
 
1.9%
6183
 
1.6%
793
 
0.8%
889
 
0.8%
940
 
0.3%
Other values (23)175
 
1.5%
ValueCountFrequency (%)
014347
53.4%
15833
21.7%
22517
 
9.4%
31426
 
5.3%
4803
 
3.0%
5493
 
1.8%
6402
 
1.5%
7253
 
0.9%
8177
 
0.7%
9150
 
0.6%
ValueCountFrequency (%)
06189
53.7%
12479
21.5%
21087
 
9.4%
3615
 
5.3%
4349
 
3.0%
5222
 
1.9%
6183
 
1.6%
793
 
0.8%
889
 
0.8%
940
 
0.3%
ValueCountFrequency (%)
06189
23.0%
12479
9.2%
21087
 
4.0%
3615
 
2.3%
4349
 
1.3%
5222
 
0.8%
6183
 
0.7%
793
 
0.3%
889
 
0.3%
940
 
0.1%
ValueCountFrequency (%)
014347
124.5%
15833
50.6%
22517
 
21.8%
31426
 
12.4%
4803
 
7.0%
5493
 
4.3%
6402
 
3.5%
7253
 
2.2%
8177
 
1.5%
9150
 
1.3%

Gold_TL
Real number (ℝ)

 TrainTest
Distinct7663
Distinct (%)0.3%0.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.59889891.5639267
 TrainTest
Minimum00
Maximum235235
Zeros194618398
Zeros (%)72.4%72.9%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:39.979273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile88
Maximum235235
Range235235
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation5.51250385.6041835
Coefficient of variation (CV)3.44768763.5834054
Kurtosis222.34339344.9942
Mean1.59889891.5639267
Median Absolute Deviation (MAD)00
Skewness10.51302112.863198
Sum4298018018
Variance30.38769831.406872
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:40.083271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019461
72.4%
12203
 
8.2%
21207
 
4.5%
3833
 
3.1%
4559
 
2.1%
5447
 
1.7%
6382
 
1.4%
7257
 
1.0%
8193
 
0.7%
9165
 
0.6%
Other values (66)1174
 
4.4%
ValueCountFrequency (%)
08398
72.9%
1939
 
8.2%
2503
 
4.4%
3351
 
3.0%
4248
 
2.2%
5178
 
1.5%
6169
 
1.5%
7103
 
0.9%
882
 
0.7%
957
 
0.5%
Other values (53)493
 
4.3%
ValueCountFrequency (%)
019461
72.4%
12203
 
8.2%
21207
 
4.5%
3833
 
3.1%
4559
 
2.1%
5447
 
1.7%
6382
 
1.4%
7257
 
1.0%
8193
 
0.7%
9165
 
0.6%
ValueCountFrequency (%)
08398
72.9%
1939
 
8.2%
2503
 
4.4%
3351
 
3.0%
4248
 
2.2%
5178
 
1.5%
6169
 
1.5%
7103
 
0.9%
882
 
0.7%
957
 
0.5%
ValueCountFrequency (%)
08398
31.2%
1939
 
3.5%
2503
 
1.9%
3351
 
1.3%
4248
 
0.9%
5178
 
0.7%
6169
 
0.6%
7103
 
0.4%
882
 
0.3%
957
 
0.2%
ValueCountFrequency (%)
019461
168.9%
12203
 
19.1%
21207
 
10.5%
3833
 
7.2%
4559
 
4.9%
5447
 
3.9%
6382
 
3.3%
7257
 
2.2%
8193
 
1.7%
9165
 
1.4%

Home_TL
Real number (ℝ)

 TrainTest
Distinct78
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0760760390.077076643
 TrainTest
Minimum00
Maximum67
Zeros2537710863
Zeros (%)94.4%94.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum67
Range67
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.359885090.35837644
Coefficient of variation (CV)4.73059724.6496115
Kurtosis59.46389259.78772
Mean0.0760760390.077076643
Median Absolute Deviation (MAD)00
Skewness6.62075036.5201044
Sum2045888
Variance0.129517280.12843368
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
025377
94.4%
11126
 
4.2%
2280
 
1.0%
354
 
0.2%
429
 
0.1%
59
 
< 0.1%
66
 
< 0.1%
ValueCountFrequency (%)
010863
94.3%
1490
 
4.3%
2131
 
1.1%
321
 
0.2%
410
 
0.1%
54
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
025377
94.4%
11126
 
4.2%
2280
 
1.0%
354
 
0.2%
429
 
0.1%
59
 
< 0.1%
66
 
< 0.1%
ValueCountFrequency (%)
010863
94.3%
1490
 
4.3%
2131
 
1.1%
321
 
0.2%
410
 
0.1%
54
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
010863
40.4%
1490
 
1.8%
2131
 
0.5%
321
 
0.1%
410
 
< 0.1%
54
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
025377
220.3%
11126
 
9.8%
2280
 
2.4%
354
 
0.5%
429
 
0.3%
59
 
0.1%
66
 
0.1%

PL_TL
Real number (ℝ)

 TrainTest
Distinct1714
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.342918790.33252322
 TrainTest
Minimum00
Maximum2321
Zeros216629343
Zeros (%)80.6%81.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile22
Maximum2321
Range2321
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.950778480.91964967
Coefficient of variation (CV)2.77260542.7656705
Kurtosis56.0480954.796859
Mean0.342918790.33252322
Median Absolute Deviation (MAD)00
Skewness5.48422485.3961008
Sum92183831
Variance0.903979710.84575552
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
021662
80.6%
13278
 
12.2%
21016
 
3.8%
3452
 
1.7%
4213
 
0.8%
5113
 
0.4%
749
 
0.2%
648
 
0.2%
825
 
0.1%
117
 
< 0.1%
Other values (7)18
 
0.1%
ValueCountFrequency (%)
09343
81.1%
11333
 
11.6%
2456
 
4.0%
3207
 
1.8%
488
 
0.8%
542
 
0.4%
622
 
0.2%
713
 
0.1%
88
 
0.1%
125
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
021662
80.6%
13278
 
12.2%
21016
 
3.8%
3452
 
1.7%
4213
 
0.8%
5113
 
0.4%
648
 
0.2%
749
 
0.2%
825
 
0.1%
97
 
< 0.1%
ValueCountFrequency (%)
09343
81.1%
11333
 
11.6%
2456
 
4.0%
3207
 
1.8%
488
 
0.8%
542
 
0.4%
622
 
0.2%
713
 
0.1%
88
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
09343
34.8%
11333
 
5.0%
2456
 
1.7%
3207
 
0.8%
488
 
0.3%
542
 
0.2%
622
 
0.1%
713
 
< 0.1%
88
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
021662
188.0%
13278
 
28.5%
21016
 
8.8%
3452
 
3.9%
4213
 
1.8%
5113
 
1.0%
648
 
0.4%
749
 
0.4%
825
 
0.2%
97
 
0.1%

Secured_TL
Real number (ℝ)

 TrainTest
Distinct8071
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.90111982.8782224
 TrainTest
Minimum00
Maximum235235
Zeros75853270
Zeros (%)28.2%28.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:40.193565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median11
Q333
95-th percentile1212
Maximum235235
Range235235
Interquartile range (IQR)33

Descriptive statistics

 TrainTest
Standard deviation6.26493216.4144757
Coefficient of variation (CV)2.15948762.2286241
Kurtosis142.63846207.14896
Mean2.90111982.8782224
Median Absolute Deviation (MAD)11
Skewness8.25816439.6912883
Sum7798533160
Variance39.24937441.145499
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:40.302574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18605
32.0%
07585
28.2%
23522
13.1%
31767
 
6.6%
41108
 
4.1%
5768
 
2.9%
6629
 
2.3%
7433
 
1.6%
8375
 
1.4%
9245
 
0.9%
Other values (70)1844
 
6.9%
ValueCountFrequency (%)
13657
31.7%
03270
28.4%
21531
13.3%
3827
 
7.2%
4458
 
4.0%
5329
 
2.9%
6265
 
2.3%
7174
 
1.5%
8152
 
1.3%
9103
 
0.9%
Other values (61)755
 
6.6%
ValueCountFrequency (%)
07585
28.2%
18605
32.0%
23522
13.1%
31767
 
6.6%
41108
 
4.1%
5768
 
2.9%
6629
 
2.3%
7433
 
1.6%
8375
 
1.4%
9245
 
0.9%
ValueCountFrequency (%)
03270
28.4%
13657
31.7%
21531
13.3%
3827
 
7.2%
4458
 
4.0%
5329
 
2.9%
6265
 
2.3%
7174
 
1.5%
8152
 
1.3%
9103
 
0.9%
ValueCountFrequency (%)
03270
12.2%
13657
13.6%
21531
5.7%
3827
 
3.1%
4458
 
1.7%
5329
 
1.2%
6265
 
1.0%
7174
 
0.6%
8152
 
0.6%
9103
 
0.4%
ValueCountFrequency (%)
07585
65.8%
18605
74.7%
23522
30.6%
31767
 
15.3%
41108
 
9.6%
5768
 
6.7%
6629
 
5.5%
7433
 
3.8%
8375
 
3.3%
9245
 
2.1%

Unsecured_TL
Real number (ℝ)

 TrainTest
Distinct4740
Distinct (%)0.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.42293812.3653329
 TrainTest
Minimum00
Maximum5546
Zeros84543604
Zeros (%)31.4%31.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median11
Q333
95-th percentile99
Maximum5546
Range5546
Interquartile range (IQR)33

Descriptive statistics

 TrainTest
Standard deviation3.83610773.67968
Coefficient of variation (CV)1.58324621.5556711
Kurtosis24.86853822.157173
Mean2.42293812.3653329
Median Absolute Deviation (MAD)11
Skewness3.93135583.7775792
Sum6513127251
Variance14.71572213.540045
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
08454
31.4%
17065
26.3%
23601
13.4%
32150
 
8.0%
41430
 
5.3%
5942
 
3.5%
6663
 
2.5%
7525
 
2.0%
8423
 
1.6%
9289
 
1.1%
Other values (37)1339
 
5.0%
ValueCountFrequency (%)
03604
31.3%
13060
26.6%
21572
13.6%
3910
 
7.9%
4611
 
5.3%
5437
 
3.8%
6304
 
2.6%
7194
 
1.7%
8169
 
1.5%
9125
 
1.1%
Other values (30)535
 
4.6%
ValueCountFrequency (%)
08454
31.4%
17065
26.3%
23601
13.4%
32150
 
8.0%
41430
 
5.3%
5942
 
3.5%
6663
 
2.5%
7525
 
2.0%
8423
 
1.6%
9289
 
1.1%
ValueCountFrequency (%)
03604
31.3%
13060
26.6%
21572
13.6%
3910
 
7.9%
4611
 
5.3%
5437
 
3.8%
6304
 
2.6%
7194
 
1.7%
8169
 
1.5%
9125
 
1.1%
ValueCountFrequency (%)
03604
13.4%
13060
11.4%
21572
5.8%
3910
 
3.4%
4611
 
2.3%
5437
 
1.6%
6304
 
1.1%
7194
 
0.7%
8169
 
0.6%
9125
 
0.5%
ValueCountFrequency (%)
08454
73.4%
17065
61.3%
23601
31.3%
32150
 
18.7%
41430
 
12.4%
5942
 
8.2%
6663
 
5.8%
7525
 
4.6%
8423
 
3.7%
9289
 
2.5%

Other_TL
Real number (ℝ)

 TrainTest
Distinct3732
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.090511.1229928
 TrainTest
Minimum00
Maximum8065
Zeros151276457
Zeros (%)56.3%56.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile55
Maximum8065
Range8065
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation2.32918522.4523359
Coefficient of variation (CV)2.13586782.1837503
Kurtosis88.36901475.473004
Mean1.090511.1229928
Median Absolute Deviation (MAD)00
Skewness6.25560216.2254186
Sum2931412938
Variance5.42510386.0139513
MonotonicityNot monotonicNot monotonic
ValueCountFrequency (%)
015127
56.3%
16171
23.0%
22329
 
8.7%
31120
 
4.2%
4675
 
2.5%
5389
 
1.4%
6276
 
1.0%
7159
 
0.6%
8141
 
0.5%
9100
 
0.4%
Other values (27)394
 
1.5%
ValueCountFrequency (%)
06457
56.0%
12623
22.8%
21013
 
8.8%
3495
 
4.3%
4295
 
2.6%
5176
 
1.5%
6108
 
0.9%
772
 
0.6%
851
 
0.4%
948
 
0.4%
Other values (22)183
 
1.6%
ValueCountFrequency (%)
015127
56.3%
16171
23.0%
22329
 
8.7%
31120
 
4.2%
4675
 
2.5%
5389
 
1.4%
6276
 
1.0%
7159
 
0.6%
8141
 
0.5%
9100
 
0.4%
ValueCountFrequency (%)
06457
56.0%
12623
22.8%
21013
 
8.8%
3495
 
4.3%
4295
 
2.6%
5176
 
1.5%
6108
 
0.9%
772
 
0.6%
851
 
0.4%
948
 
0.4%
ValueCountFrequency (%)
06457
24.0%
12623
9.8%
21013
 
3.8%
3495
 
1.8%
4295
 
1.1%
5176
 
0.7%
6108
 
0.4%
772
 
0.3%
851
 
0.2%
948
 
0.2%
ValueCountFrequency (%)
015127
131.3%
16171
53.6%
22329
 
20.2%
31120
 
9.7%
4675
 
5.9%
5389
 
3.4%
6276
 
2.4%
7159
 
1.4%
8141
 
1.2%
9100
 
0.9%

Age_Oldest_TL
Real number (ℝ)

 TrainTest
Distinct261242
Distinct (%)1.0%2.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-16.256724-65.979168
 TrainTest
Minimum-99999-99999
Maximum370359
Zeros83
Zeros (%)< 0.1%< 0.1%
Negative1713
Negative (%)0.1%0.1%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:40.412469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile55
Q11414
median3535
Q36666
95-th percentile139138
Maximum370359
Range100369100358
Interquartile range (IQR)5252

Descriptive statistics

 TrainTest
Standard deviation2515.56313359.1911
Coefficient of variation (CV)-154.73985-50.912904
Kurtosis1575.6137881.32912
Mean-16.256724-65.979168
Median Absolute Deviation (MAD)2323
Skewness-39.71193-29.715864
Sum-436997-760146
Variance6328057.511284165
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:40.520618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7729
 
2.7%
10665
 
2.5%
8654
 
2.4%
9570
 
2.1%
6564
 
2.1%
11554
 
2.1%
14547
 
2.0%
12533
 
2.0%
13508
 
1.9%
15508
 
1.9%
Other values (251)21049
78.3%
ValueCountFrequency (%)
7318
 
2.8%
8274
 
2.4%
9269
 
2.3%
11256
 
2.2%
10247
 
2.1%
6242
 
2.1%
13238
 
2.1%
12233
 
2.0%
16221
 
1.9%
14212
 
1.8%
Other values (232)9011
78.2%
ValueCountFrequency (%)
-9999917
 
0.1%
08
 
< 0.1%
184
 
0.3%
2312
1.2%
3351
1.3%
4373
1.4%
5377
1.4%
6564
2.1%
7729
2.7%
8654
2.4%
ValueCountFrequency (%)
-9999913
 
0.1%
03
 
< 0.1%
136
 
0.3%
2121
 
1.1%
3155
1.3%
4160
1.4%
5177
1.5%
6242
2.1%
7318
2.8%
8274
2.4%
ValueCountFrequency (%)
-9999913
 
< 0.1%
03
 
< 0.1%
136
 
0.1%
2121
 
0.5%
3155
0.6%
4160
0.6%
5177
0.7%
6242
0.9%
7318
1.2%
8274
1.0%
ValueCountFrequency (%)
-9999917
 
0.1%
08
 
0.1%
184
 
0.7%
2312
2.7%
3351
3.0%
4373
3.2%
5377
3.3%
6564
4.9%
7729
6.3%
8654
5.7%

Age_Newest_TL
Real number (ℝ)

 TrainTest
Distinct182165
Distinct (%)0.7%1.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-48.258733-97.785088
 TrainTest
Minimum-99999-99999
Maximum370359
Zeros7436
Zeros (%)0.3%0.3%
Negative1713
Negative (%)0.1%0.1%
Memory size420.0 KiB180.0 KiB
2026-01-17T22:57:40.627221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-99999-99999
5-th percentile22
Q144
median78
Q31616
95-th percentile5756
Maximum370359
Range100369100358
Interquartile range (IQR)1212

Descriptive statistics

 TrainTest
Standard deviation2514.48443357.9196
Coefficient of variation (CV)-52.104237-34.339793
Kurtosis1576.3005881.54223
Mean-48.258733-97.785088
Median Absolute Deviation (MAD)45
Skewness-39.724909-29.721249
Sum-1297243-1126582
Variance632263211275624
MonotonicityNot monotonicNot monotonic
2026-01-17T22:57:40.745233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22910
 
10.8%
32597
 
9.7%
42152
 
8.0%
51723
 
6.4%
61651
 
6.1%
71415
 
5.3%
81307
 
4.9%
1978
 
3.6%
10969
 
3.6%
9914
 
3.4%
Other values (172)10265
38.2%
ValueCountFrequency (%)
21211
 
10.5%
31067
 
9.3%
4930
 
8.1%
5752
 
6.5%
6717
 
6.2%
7581
 
5.0%
8559
 
4.9%
9468
 
4.1%
1410
 
3.6%
10399
 
3.5%
Other values (155)4427
38.4%
ValueCountFrequency (%)
-9999917
 
0.1%
074
 
0.3%
1978
 
3.6%
22910
10.8%
32597
9.7%
42152
8.0%
51723
6.4%
61651
6.1%
71415
5.3%
81307
4.9%
ValueCountFrequency (%)
-9999913
 
0.1%
036
 
0.3%
1410
 
3.6%
21211
10.5%
31067
9.3%
4930
8.1%
5752
6.5%
6717
6.2%
7581
5.0%
8559
4.9%
ValueCountFrequency (%)
-9999913
 
< 0.1%
036
 
0.1%
1410
 
1.5%
21211
4.5%
31067
4.0%
4930
3.5%
5752
2.8%
6717
2.7%
7581
2.2%
8559
2.1%
ValueCountFrequency (%)
-9999917
 
0.1%
074
 
0.6%
1978
 
8.5%
22910
25.3%
32597
22.5%
42152
18.7%
51723
15.0%
61651
14.3%
71415
12.3%
81307
11.3%

Correlations

Train

2026-01-17T22:57:40.907862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2026-01-17T22:57:41.720464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Train

AGEAge_Newest_TLAge_Oldest_TLAuto_TLCC_FlagCC_TLCC_enqCC_enq_L12mCC_enq_L6mCC_utilizationConsumer_TLCredit_ScoreEDUCATIONGENDERGL_FlagGold_TLHL_FlagHome_TLMARITALSTATUSNETMONTHLYINCOMEOther_TLPL_FlagPL_TLPL_enqPL_enq_L12mPL_enq_L6mPL_utilizationPROSPECTIDSecured_TLTime_With_Curr_EmprTot_Active_TLTot_Closed_TLTot_Missed_PmntTot_TL_closed_L12MTot_TL_closed_L6MTotal_TLTotal_TL_opened_L12MTotal_TL_opened_L6MUnsecured_TLenq_L12menq_L3menq_L6mfirst_prod_enq2last_prod_enq2max_delinquency_levelmax_deliq_12mtsmax_deliq_6mtsmax_recent_level_of_deliqmax_unsec_exposure_inPctnum_dbtnum_dbt_12mtsnum_dbt_6mtsnum_deliq_12mtsnum_deliq_6_12mtsnum_deliq_6mtsnum_lssnum_lss_12mtsnum_lss_6mtsnum_stdnum_std_12mtsnum_std_6mtsnum_subnum_sub_12mtsnum_sub_6mtsnum_times_30p_dpdnum_times_60p_dpdnum_times_delinquentpct_CC_enq_L6m_of_L12mpct_CC_enq_L6m_of_everpct_PL_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_active_tlpct_closed_tlpct_currentBal_all_TLpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_tl_closed_L12Mpct_tl_closed_L6Mpct_tl_open_L12Mpct_tl_open_L6Mrecent_level_of_deliqresponse_flagtime_since_first_deliquencytime_since_recent_deliquencytime_since_recent_enqtime_since_recent_paymenttot_enq
AGE1.0000.1090.3520.0370.044-0.020-0.067-0.096-0.087-0.0390.0060.2690.0570.1030.1430.0940.1080.1340.5810.1520.1420.1020.097-0.024-0.098-0.1030.062-0.0010.1620.4280.0410.2230.0130.0540.0210.169-0.064-0.0760.065-0.141-0.109-0.1360.0660.0420.073-0.032-0.0450.0730.0400.0340.0090.0000.0290.0270.0360.0090.0030.0140.1590.1220.1070.0270.0060.0120.0720.0680.075-0.091-0.093-0.103-0.112-0.1910.191-0.052-0.191-0.0900.0140.001-0.205-0.1330.0690.0320.0780.0640.0620.099-0.028
Age_Newest_TL0.1091.0000.215-0.0190.008-0.212-0.279-0.305-0.295-0.218-0.4200.1650.0000.0000.000-0.1220.012-0.0160.012-0.084-0.2130.009-0.219-0.341-0.361-0.342-0.2400.002-0.1070.089-0.614-0.123-0.521-0.306-0.316-0.439-0.825-0.846-0.461-0.502-0.367-0.4680.0000.000-0.009-0.299-0.390-0.008-0.4270.0180.0190.000-0.036-0.001-0.0560.0370.0250.000-0.070-0.124-0.1450.0340.0210.0030.0010.002-0.011-0.254-0.255-0.302-0.302-0.2480.248-0.502-0.248-0.815-0.203-0.264-0.683-0.815-0.0020.000-0.004-0.0010.1410.397-0.409
Age_Oldest_TL0.3520.2151.0000.3120.0080.1480.0800.005-0.0020.1150.0100.4790.0000.0000.0000.3150.0120.1680.0120.1460.3200.0090.2000.069-0.045-0.0470.127-0.0000.5280.2010.1940.6440.0650.2120.1240.531-0.089-0.0740.187-0.104-0.062-0.0800.0000.0000.297-0.029-0.0840.2930.1150.0760.0290.0000.1210.1270.0940.0510.0240.0000.3590.2420.2030.1010.0370.0140.2720.2440.2990.0120.007-0.054-0.070-0.5320.532-0.098-0.532-0.1460.0940.059-0.579-0.2630.2810.0000.3150.2870.0750.2090.138
Auto_TL0.037-0.0190.3121.0000.1430.0910.1490.1370.1430.083-0.0120.1230.0140.0480.073-0.0120.0660.0190.0610.085-0.0280.0930.0460.0980.0810.0990.0330.0010.4470.0560.2010.1870.0730.0930.0680.2190.0730.042-0.0200.1030.0800.0900.0720.0320.1670.1480.1530.173-0.039-0.015-0.0080.0000.1160.0990.106-0.021-0.0070.0000.0660.0520.047-0.004-0.015-0.0050.1140.0710.1820.0490.0470.0160.011-0.0700.0700.023-0.070-0.0110.0270.030-0.120-0.0460.1670.0160.1840.1640.136-0.0830.246
CC_Flag0.0440.0080.0080.1431.0000.4800.1490.1490.1490.9490.2270.1120.2060.0650.0860.0240.0000.1020.0250.0840.0300.2480.1720.1490.1490.1490.2441.0000.0290.0430.3270.0620.0490.1600.1740.1200.2290.2000.3470.1490.1490.1490.4550.4180.1200.2180.2440.0250.3780.0000.0000.0000.1490.1220.1500.0000.0000.0000.0410.0340.0410.0120.0000.0000.0410.0420.0960.4780.4800.2040.2440.2660.2570.0000.2660.2330.1890.1690.2200.1980.0280.1770.1200.1200.1490.0890.149
CC_TL-0.020-0.2120.1480.0910.4801.0000.6760.5160.4200.9420.149-0.0160.0610.0320.1420.0080.0160.0940.0260.1760.0310.2150.2690.3120.2420.2130.2490.0030.034-0.0490.3860.1540.1140.1470.1440.3070.2570.2090.4260.2990.2010.2500.1300.0900.1110.2320.2660.1040.408-0.011-0.0080.0000.1580.1330.162-0.013-0.0100.0000.0130.0090.011-0.030-0.018-0.0120.0660.0350.1320.4630.4580.1480.1350.066-0.0660.1320.0660.1330.0530.0910.0560.1200.0940.1180.1200.0950.013-0.2670.401
CC_enq-0.067-0.2790.0800.1490.1490.6761.0000.8990.8310.6510.307-0.1570.0850.0450.018-0.0460.1290.0740.0350.2050.0210.1360.3100.6150.5800.5730.2840.006-0.009-0.0830.4000.1490.1190.1890.1900.3130.3190.2650.4810.6110.5310.5720.3360.4410.1250.3050.3380.1230.423-0.020-0.0080.0000.1670.1420.152-0.006-0.0060.000-0.037-0.027-0.020-0.010-0.008-0.0060.0690.0330.1450.6010.5990.2930.2850.078-0.0780.1000.0780.1860.1060.1440.1360.1850.1150.0370.1320.1110.244-0.2300.683
CC_enq_L12m-0.096-0.3050.0050.1370.1490.5160.8991.0000.9210.5170.313-0.2140.0850.0450.018-0.0700.1290.0400.0350.1620.0130.1360.2620.6090.6100.6110.2480.007-0.037-0.0980.3530.1020.1200.1770.1830.2590.3450.2890.4250.6670.5820.6260.3360.4410.0840.2740.3080.0850.369-0.018-0.0050.0000.1270.0990.114-0.002-0.0030.000-0.060-0.045-0.037-0.006-0.004-0.0030.0340.0080.0970.6850.6860.3100.3060.094-0.0940.0900.0940.2080.1110.1450.2060.2230.0810.0370.0860.0760.248-0.2010.657
CC_enq_L6m-0.087-0.295-0.0020.1430.1490.4200.8310.9211.0000.4210.312-0.2240.0850.0450.018-0.0800.1290.0310.0350.1530.0080.1360.2350.6050.6150.6300.2220.004-0.040-0.0880.3220.0870.1200.1610.1700.2330.3110.2940.3920.6460.6120.6530.3360.4410.0760.2600.2900.0780.333-0.020-0.0040.0000.1090.0860.092-0.003-0.0000.000-0.064-0.049-0.041-0.0010.002-0.0000.0300.0060.0860.7590.7590.3080.3050.089-0.0890.0740.0890.2270.1040.1370.1870.2400.0750.0370.0790.0700.280-0.1770.636
CC_utilization-0.039-0.2180.1150.0830.9490.9420.6510.5170.4211.0000.150-0.0400.2030.0680.0710.0080.0000.0690.0290.1610.0310.2470.2560.3100.2480.2200.2430.0010.025-0.0560.3770.1320.1190.1380.1390.2880.2620.2140.4040.3060.2070.2580.4280.4050.0980.2230.2560.0930.409-0.009-0.0070.0000.1500.1250.152-0.012-0.0100.0000.0030.0030.005-0.030-0.018-0.0110.0510.0170.1180.4690.4660.1590.1480.081-0.0810.1560.0810.1370.0480.0870.0740.1300.0850.1850.1080.0860.002-0.2690.392
Consumer_TL0.006-0.4200.010-0.0120.2270.1490.3070.3130.3120.1501.000-0.1450.0230.0240.025-0.0790.026-0.0160.0350.0970.0990.2460.1900.4440.4080.3630.1980.008-0.149-0.0370.4740.2670.1970.3900.3900.4380.5150.4130.7600.4560.3190.3890.0710.0690.0540.3090.3500.0570.496-0.027-0.0200.0000.0940.0820.065-0.026-0.0070.000-0.123-0.124-0.119-0.032-0.014-0.0090.001-0.0150.0630.1740.1740.2670.261-0.0130.013-0.061-0.0130.2690.3130.3460.2610.2950.0560.0840.0540.0540.028-0.1380.521
Credit_Score0.2690.1650.4790.1230.112-0.016-0.157-0.214-0.224-0.040-0.1451.0000.0360.0180.1920.1620.1620.1290.1580.0370.1930.1290.036-0.228-0.334-0.374-0.009-0.0280.2850.1850.0470.2570.0090.0690.0180.184-0.097-0.152-0.034-0.450-0.513-0.4900.0890.118-0.202-0.172-0.150-0.199-0.0200.0380.0030.000-0.089-0.088-0.0300.015-0.0010.0000.4020.3830.3640.0490.0130.006-0.124-0.083-0.187-0.192-0.195-0.385-0.393-0.2080.2080.030-0.208-0.1600.019-0.007-0.274-0.224-0.2110.175-0.203-0.2300.2980.103-0.274
EDUCATION0.0570.0000.0000.0140.2060.0610.0850.0850.0850.2030.0230.0361.0000.0560.0850.0130.0630.0400.1600.0270.0160.1170.0450.0850.0850.0850.1041.0000.0130.0420.0380.0150.0090.0200.0280.0170.0250.0330.0460.0850.0850.0850.0740.0660.0310.0630.0690.0220.0800.0030.0000.0050.0230.0130.0290.0150.0000.0090.0250.0270.0290.0100.0000.0000.0050.0090.0120.0620.0610.0460.0490.0410.0390.0000.0410.0290.0300.0220.0340.0270.0180.0520.0310.0310.0850.0410.085
GENDER0.1030.0000.0000.0480.0650.0320.0450.0450.0450.0680.0240.0180.0561.0000.0000.0320.0000.0000.1320.0210.0090.0190.0000.0450.0450.0450.0101.0000.0180.0520.0290.0220.0210.0100.0140.0210.0180.0270.0310.0450.0450.0450.0770.0500.0260.0190.0300.0140.0110.0000.0000.0000.0090.0050.0260.0000.0000.0000.0220.0240.0260.0000.0000.0000.0300.0280.0370.0490.0500.0420.0400.0360.0360.0000.0360.0350.0320.0250.0400.0260.0180.0160.0260.0260.0450.0040.045
GL_Flag0.1430.0000.0000.0730.0860.1420.0180.0180.0180.0710.0250.1920.0850.0001.0000.0190.0121.0000.0750.0500.0560.0830.1040.0180.0180.0180.0481.0000.0530.1010.1280.0260.0370.0250.0340.0580.0470.0430.0760.0180.0180.0180.4220.2780.1130.0480.0520.0220.0000.0000.0240.0020.1070.1000.0890.0000.0000.0000.1630.1550.1480.0000.0000.0000.0550.0430.1050.0320.0440.0230.0420.1140.1140.0000.1140.0270.0850.0610.1160.0830.0230.0080.1130.1130.0180.0310.018
Gold_TL0.094-0.1220.315-0.0120.0240.008-0.046-0.070-0.0800.008-0.0790.1620.0130.0320.0191.0000.1670.0180.0360.0370.1380.0280.026-0.031-0.062-0.0700.0160.0050.6720.0210.1970.5490.2340.2500.1520.4970.1690.128-0.030-0.043-0.034-0.0320.0170.0140.220-0.025-0.0560.212-0.0230.0200.0020.0000.0400.0330.028-0.0110.0040.0440.3360.2860.2630.018-0.011-0.0090.2370.2260.206-0.004-0.005-0.021-0.026-0.3710.3710.162-0.3710.0720.1300.092-0.1440.0040.1970.0090.2300.218-0.1040.046-0.018
HL_Flag0.1080.0120.0120.0660.0000.0160.1290.1290.1290.0000.0260.1620.0630.0000.0120.1671.0000.0100.0880.0210.1160.0150.0200.1290.1290.1290.0121.0000.1980.0120.1760.2010.1460.1390.1180.2200.1400.1130.0550.1290.1290.1290.0500.0510.1700.0950.1010.0920.0970.0000.0000.0000.0430.0430.0390.0200.0130.0160.2490.2640.2420.0140.0150.0000.1030.1040.1250.0280.0330.0360.0590.4460.4530.0130.4460.1460.2200.1430.3200.2060.0490.0000.1700.1700.1290.0100.129
Home_TL0.134-0.0160.1680.0190.1020.0940.0740.0400.0310.069-0.0160.1290.0400.0001.0000.0180.0101.0000.0760.1090.0490.0990.0910.0530.0050.0070.046-0.0130.1900.0990.1540.0870.0750.0320.0100.1370.0320.0120.0490.0110.0000.0060.1940.1260.1190.1050.0960.1180.0360.0080.0070.0000.1150.1080.110-0.004-0.0050.0000.1470.1470.1420.000-0.004-0.0020.0990.0710.1240.0250.024-0.005-0.0110.009-0.0090.1420.0090.000-0.009-0.010-0.058-0.0240.1120.0080.1150.0970.029-0.0480.095
MARITALSTATUS0.5810.0120.0120.0610.0250.0260.0350.0350.0350.0290.0350.1580.1600.1320.0750.0360.0880.0761.0000.0180.0470.0570.0420.0350.0350.0350.0491.0000.0410.2510.0450.0500.0220.0250.0230.0550.0090.0060.0480.0350.0350.0350.0820.0750.0620.0440.0450.0430.0150.0000.0150.0110.0470.0440.0390.0100.0000.0000.0960.0840.0740.0060.0000.0050.0510.0450.0710.0890.0800.0720.0920.1500.1530.0000.1500.0550.0670.0480.1710.1320.0350.0310.0620.0620.0350.0200.035
NETMONTHLYINCOME0.152-0.0840.1460.0850.0840.1760.2050.1620.1530.1610.0970.0370.0270.0210.0500.0370.0210.1090.0181.0000.0660.0380.1510.1710.1230.1150.1240.0040.0870.2660.1770.1280.0780.0750.0710.1830.1110.0920.1780.1420.1040.1260.0300.0210.0780.0850.0970.0790.0750.0140.0110.0000.0530.0530.049-0.0010.0070.0000.0580.0540.0520.0070.0090.0130.0650.0600.0840.1280.1270.0770.072-0.0210.0210.057-0.0210.0540.0280.047-0.0060.0440.0720.0140.0850.0710.034-0.0670.218
Other_TL0.142-0.2130.320-0.0280.0300.0310.0210.0130.0080.0310.0990.1930.0160.0090.0560.1380.1160.0490.0470.0661.0000.0280.0740.0730.0380.0290.049-0.0040.3470.0720.4320.3650.3350.2800.2330.4900.3080.2610.3110.0720.0430.0670.0230.0250.1600.0990.0830.1570.2540.0670.0240.0000.1000.0890.0810.0400.0120.0000.3890.3370.3020.0980.0550.0380.1520.1450.1590.0380.0370.0390.035-0.1090.1090.227-0.1090.1540.1530.165-0.0300.1240.1490.0050.1530.139-0.061-0.0560.133
PL_Flag0.1020.0090.0090.0930.2480.2150.1360.1360.1360.2470.2460.1290.1170.0190.0830.0280.0150.0990.0570.0380.0281.0000.3840.1360.1360.1360.8831.0000.0330.0380.3120.0740.0620.1980.2290.1270.2670.2530.3860.1360.1360.1360.2830.2680.0950.1190.1310.0060.3270.0000.0000.0000.0860.0780.0730.0000.0000.0070.1210.1020.1080.0110.0000.0110.0210.0270.0880.2110.2230.3270.3730.3070.3110.0110.3070.2540.2510.2210.2580.2260.0060.1400.0950.0950.1360.0910.136
PL_TL0.097-0.2190.2000.0460.1720.2690.3100.2620.2350.2560.1900.0360.0450.0000.1040.0260.0200.0910.0420.1510.0740.3841.0000.5300.3690.3080.8780.0030.0450.0350.3600.2810.1640.2590.2350.3670.2690.2430.4880.2640.1820.2310.0880.0530.0960.1420.1500.0980.505-0.012-0.0080.0000.1070.0900.098-0.007-0.0070.0000.1620.1590.156-0.010-0.018-0.0060.0590.0360.1090.2090.2050.2650.247-0.0620.0620.139-0.0620.1600.1670.1820.0280.1400.0910.0930.1030.0860.005-0.1390.386
PL_enq-0.024-0.3410.0690.0980.1490.3120.6150.6090.6050.3100.444-0.2280.0850.0450.018-0.0310.1290.0530.0350.1710.0730.1360.5301.0000.8960.8140.4860.007-0.022-0.0500.4250.2130.1630.2630.2630.3700.3940.3390.5600.6810.5570.6330.3360.4410.1070.2700.3020.1100.501-0.0110.0000.0000.1280.1070.115-0.0060.0110.0000.0080.0240.027-0.008-0.014-0.0040.0600.0370.1200.3250.3240.6430.6350.035-0.0350.0960.0350.2420.1750.2150.1890.2490.1040.0370.1110.0960.156-0.1700.746
PL_enq_L12m-0.098-0.361-0.0450.0810.1490.2420.5800.6100.6150.2480.408-0.3340.0850.0450.018-0.0620.1290.0050.0350.1230.0380.1360.3690.8961.0000.9010.3720.010-0.066-0.0940.3620.1050.1420.2090.2230.2700.4070.3530.4580.7430.6060.6890.3360.4410.0630.2490.2860.0650.414-0.011-0.0040.0000.0900.0720.079-0.0090.0040.000-0.065-0.043-0.032-0.010-0.011-0.0030.0320.0150.0720.3160.3150.7430.7450.098-0.0980.0960.0980.2650.1500.1900.2750.2930.0610.0370.0650.0560.136-0.1590.684
PL_enq_L6m-0.103-0.342-0.0470.0990.1490.2130.5730.6110.6300.2200.363-0.3740.0850.0450.018-0.0700.1290.0070.0350.1150.0290.1360.3080.8140.9011.0000.3040.015-0.060-0.0970.3190.0810.1390.1750.1970.2330.3530.3580.4040.7090.6510.7380.3360.4410.0650.2370.2680.0660.346-0.010-0.0010.0000.0890.0720.076-0.0040.0090.000-0.071-0.048-0.037-0.002-0.0030.0020.0350.0160.0720.3040.3040.8580.8580.091-0.0910.0830.0910.2900.1280.1710.2390.3120.0630.0370.0660.0560.144-0.1500.656
PL_utilization0.062-0.2400.1270.0330.2440.2490.2840.2480.2220.2430.198-0.0090.1040.0100.0480.0160.0120.0460.0490.1240.0490.8830.8780.4860.3720.3041.0000.0060.0160.0120.3740.1980.1700.2050.1890.3220.2820.2520.4390.2660.1770.2300.2300.2570.0680.1350.1520.0710.547-0.017-0.0060.0000.0840.0630.079-0.008-0.0090.0000.1080.1160.117-0.019-0.020-0.0130.0310.0110.0790.1990.1960.2710.2540.019-0.0190.1740.0190.1720.1190.1390.0680.1600.0660.1500.0760.063-0.003-0.1580.352
PROSPECTID-0.0010.002-0.0000.0011.0000.0030.0060.0070.0040.0010.008-0.0281.0001.0001.0000.0051.000-0.0131.0000.004-0.0041.0000.0030.0070.0100.0150.0061.0000.0010.0070.001-0.0010.0010.002-0.0030.003-0.0010.0030.0040.0150.0220.0171.0001.0000.0150.000-0.0110.0140.0030.0030.0031.0000.0040.014-0.010-0.007-0.0161.000-0.010-0.007-0.0060.0000.000-0.0010.0090.0120.0120.0020.0020.0160.0170.000-0.000-0.0100.0000.002-0.001-0.005-0.0040.0030.0151.0000.0140.018-0.011-0.0020.011
Secured_TL0.162-0.1070.5280.4470.0290.034-0.009-0.037-0.0400.025-0.1490.2850.0130.0180.0530.6720.1980.1900.0410.0870.3470.0330.045-0.022-0.066-0.0600.0160.0011.0000.0920.3170.6210.2690.2800.1740.6160.1800.130-0.100-0.033-0.033-0.0270.0190.0160.2910.0490.0260.286-0.0940.0330.0090.0000.1000.0860.0890.0010.0070.0350.4200.3560.3230.039-0.0010.0030.2800.2480.2860.0040.002-0.045-0.051-0.3750.3750.226-0.3750.0540.1350.100-0.231-0.0320.2700.0000.3050.283-0.0290.0060.089
Time_With_Curr_Empr0.4280.0890.2010.0560.043-0.049-0.083-0.098-0.088-0.056-0.0370.1850.0420.0520.1010.0210.0120.0990.2510.2660.0720.0380.035-0.050-0.094-0.0970.0120.0070.0921.0000.0020.0920.0070.005-0.0130.060-0.065-0.063-0.004-0.127-0.110-0.1250.0540.0470.031-0.034-0.0340.031-0.0240.0160.0020.0000.0070.0080.0150.0010.0100.0000.0880.0770.0730.0230.0090.0150.0310.0320.033-0.097-0.099-0.095-0.099-0.0890.089-0.028-0.089-0.065-0.012-0.020-0.129-0.0880.0290.0240.0360.0260.0700.050-0.056
Tot_Active_TL0.041-0.6140.1940.2010.3270.3860.4000.3530.3220.3770.4740.0470.0380.0290.1280.1970.1760.1540.0450.1770.4320.3120.3600.4250.3620.3190.3740.0010.3170.0021.0000.2740.5290.2980.2700.7420.7300.5940.6650.4400.2890.3730.0450.0630.2090.3930.4410.2080.6590.008-0.0010.0000.1980.1630.176-0.0020.0010.0000.2700.2880.2900.0190.0030.0040.1480.1160.2220.2610.2580.2470.2370.286-0.2860.4150.2860.4210.0940.1630.2820.4140.1950.1160.2100.188-0.018-0.3410.533
Tot_Closed_TL0.223-0.1230.6440.1870.0620.1540.1490.1020.0870.1320.2670.2570.0150.0220.0260.5490.2010.0870.0500.1280.3650.0740.2810.2130.1050.0810.198-0.0010.6210.0920.2741.0000.1940.6550.5140.7920.2450.1820.3690.0890.0670.0830.0150.0150.3030.0760.0320.2960.1610.028-0.0020.0000.1150.1140.085-0.0060.0060.0310.3330.2350.1950.0360.0020.0060.2780.2450.3010.0920.0890.0640.049-0.7740.7740.008-0.7740.0530.5240.445-0.301-0.0450.2810.0230.3200.299-0.0070.1000.282
Tot_Missed_Pmnt0.013-0.5210.0650.0730.0490.1140.1190.1200.1200.1190.1970.0090.0090.0210.0370.2340.1460.0750.0220.0780.3350.0620.1640.1630.1420.1390.1700.0010.2690.0070.5290.1941.0000.2150.1900.4240.4970.5100.2820.2180.1940.2250.0150.0090.0770.1400.1710.0750.2620.006-0.0040.0000.0450.0390.048-0.014-0.0030.0000.2060.2150.218-0.001-0.004-0.0000.0750.0760.0780.1360.1350.1350.1330.101-0.1010.4070.1010.4040.0930.1240.2290.4020.0660.0260.0770.066-0.102-0.1990.217
Tot_TL_closed_L12M0.054-0.3060.2120.0930.1600.1470.1890.1770.1610.1380.3900.0690.0200.0100.0250.2500.1390.0320.0250.0750.2800.1980.2590.2630.2090.1750.2050.0020.2800.0050.2980.6550.2151.0000.8010.5540.4270.3100.4160.2150.1490.1810.0400.0420.1740.2380.1860.1690.1950.007-0.0020.0000.1350.1360.087-0.012-0.0050.0000.2210.2130.1670.0110.0100.0090.1330.1110.1790.1340.1320.1350.126-0.4500.4500.082-0.4500.1560.9330.7630.0200.1260.1590.0930.1720.162-0.020-0.0740.315
Tot_TL_closed_L6M0.021-0.3160.1240.0680.1740.1440.1900.1830.1700.1390.3900.0180.0280.0140.0340.1520.1180.0100.0230.0710.2330.2290.2350.2630.2230.1970.189-0.0030.174-0.0130.2700.5140.1900.8011.0000.4470.4170.3420.3940.2310.1640.1980.0530.0580.1030.1960.2100.1010.1910.0040.0010.0000.0990.0950.066-0.0080.0010.0000.1400.1450.140-0.0030.0030.0110.0650.0440.1080.1410.1400.1570.150-0.3360.3360.049-0.3360.1930.7340.9730.0900.1780.0950.0960.1010.097-0.020-0.1110.300
Total_TL0.169-0.4390.5310.2190.1200.3070.3130.2590.2330.2880.4380.1840.0170.0210.0580.4970.2200.1370.0550.1830.4900.1270.3670.3700.2700.2330.3220.0030.6160.0600.7420.7920.4240.5540.4471.0000.5740.4560.6200.3090.2090.2670.0200.0210.3240.2770.2730.3190.4740.022-0.0010.0000.1860.1660.152-0.0050.0050.0240.3700.3100.2810.0370.0070.0090.2750.2350.3280.2030.1990.1840.171-0.3220.3220.244-0.3220.2830.3570.347-0.0270.2220.3030.0450.3340.311-0.020-0.1090.484
Total_TL_opened_L12M-0.064-0.825-0.0890.0730.2290.2570.3190.3450.3110.2620.515-0.0970.0250.0180.0470.1690.1400.0320.0090.1110.3080.2670.2690.3940.4070.3530.282-0.0010.180-0.0650.7300.2450.4970.4270.4170.5741.0000.7540.5680.5510.3380.4440.0410.0650.0420.3060.3890.0420.493-0.012-0.0130.0000.0750.0340.086-0.027-0.0180.0000.1060.1510.166-0.032-0.025-0.0120.0190.0130.0450.2690.2700.3020.3010.162-0.1620.4170.1620.5760.2820.3440.7030.6210.0340.1350.0360.032-0.112-0.3450.467
Total_TL_opened_L6M-0.076-0.846-0.0740.0420.2000.2090.2650.2890.2940.2140.413-0.1520.0330.0270.0430.1280.1130.0120.0060.0920.2610.2530.2430.3390.3530.3580.2520.0030.130-0.0630.5940.1820.5100.3100.3420.4560.7541.0000.4620.4640.3440.4890.0340.0630.0060.1850.2690.0060.404-0.003-0.0070.000-0.001-0.0290.028-0.024-0.0160.0000.0670.0950.117-0.016-0.012-0.0020.0040.0050.0070.2880.2890.3380.3380.141-0.1410.3770.1410.9120.1860.2720.4930.9200.0010.1320.0090.005-0.137-0.2860.402
Unsecured_TL0.065-0.4610.187-0.0200.3470.4260.4810.4250.3920.4040.760-0.0340.0460.0310.076-0.0300.0550.0490.0480.1780.3110.3860.4880.5600.4580.4040.4390.004-0.100-0.0040.6650.3690.2820.4160.3940.6200.5680.4621.0000.4810.3370.4130.0710.0830.1530.3520.3720.1520.7960.006-0.0100.0000.1740.1520.136-0.009-0.0070.0000.0930.0840.0820.0180.0080.0050.0900.0630.1660.2920.2890.3060.294-0.0030.0030.103-0.0030.3100.2950.3300.1980.3110.1460.1280.1530.1400.021-0.2120.606
enq_L12m-0.141-0.502-0.1040.1030.1490.2990.6110.6670.6460.3060.456-0.4500.0850.0450.018-0.0430.1290.0110.0350.1420.0720.1360.2640.6810.7430.7090.2660.015-0.033-0.1270.4400.0890.2180.2150.2310.3090.5510.4640.4811.0000.8140.9100.3360.4410.0950.3100.3510.0950.418-0.012-0.0090.0000.1410.1100.119-0.013-0.0020.000-0.080-0.054-0.043-0.0020.002-0.0010.0530.0320.1050.4140.4150.5240.5250.153-0.1530.1880.1530.3670.1430.1940.4320.4070.0900.0370.0900.080-0.164-0.2690.842
enq_L3m-0.109-0.367-0.0620.0800.1490.2010.5310.5820.6120.2070.319-0.5130.0850.0450.018-0.0340.1290.0000.0350.1040.0430.1360.1820.5570.6060.6510.1770.022-0.033-0.1100.2890.0670.1940.1490.1640.2090.3380.3440.3370.8141.0000.8960.3360.4410.0930.2250.2390.0920.280-0.0090.0000.0000.1180.1050.089-0.0050.0020.000-0.062-0.044-0.0360.0080.010-0.0010.0590.0390.1000.3490.3500.4500.4500.091-0.0910.1170.0910.2880.1010.1380.2490.3130.0880.0370.0890.080-0.252-0.1790.698
enq_L6m-0.136-0.468-0.0800.0900.1490.2500.5720.6260.6530.2580.389-0.4900.0850.0450.018-0.0320.1290.0060.0350.1260.0670.1360.2310.6330.6890.7380.2300.017-0.027-0.1250.3730.0830.2250.1810.1980.2670.4440.4890.4130.9100.8961.0000.3360.4410.0880.2560.2880.0870.351-0.014-0.0030.0000.1150.0920.096-0.009-0.0010.000-0.072-0.052-0.0410.0050.0090.0020.0560.0370.0960.4220.4220.5670.5680.120-0.1200.1680.1200.4280.1180.1640.3270.4530.0820.0370.0840.074-0.222-0.2230.776
first_prod_enq20.0660.0000.0000.0720.4550.1300.3360.3360.3360.4280.0710.0890.0740.0770.4220.0170.0500.1940.0820.0300.0230.2830.0880.3360.3360.3360.2301.0000.0190.0540.0450.0150.0150.0400.0530.0200.0410.0340.0710.3360.3360.3361.0000.3050.1020.1570.1610.0260.1300.0170.0030.0110.0520.0410.0530.0150.0140.0140.0620.0620.0520.0110.0000.0080.0380.0280.0600.1060.1110.0970.1030.0770.0770.0000.0770.0600.0830.0740.0790.0650.0200.0780.1020.1020.3360.0410.336
last_prod_enq20.0420.0000.0000.0320.4180.0900.4410.4410.4410.4050.0690.1180.0660.0500.2780.0140.0510.1260.0750.0210.0250.2680.0530.4410.4410.4410.2571.0000.0160.0470.0630.0150.0090.0420.0580.0210.0650.0630.0830.4410.4410.4410.3051.0000.0710.2120.2210.0160.1560.0000.0000.0180.0310.0300.0230.0090.0090.0040.0270.0220.0230.0000.0000.0150.0190.0200.0210.2190.2210.2200.2190.0830.0840.0130.0830.0870.0690.0660.1050.0840.0130.0960.0710.0710.4410.0900.441
max_delinquency_level0.073-0.0090.2970.1670.1200.1110.1250.0840.0760.0980.054-0.2020.0310.0260.1130.2200.1700.1190.0620.0780.1600.0950.0960.1070.0630.0650.0680.0150.2910.0310.2090.3030.0770.1740.1030.3240.0420.0060.1530.0950.0930.0880.1020.0711.0000.5180.3330.9890.1240.031-0.0040.0000.6540.5570.4860.0000.0060.0140.1320.1040.0870.0510.0190.0200.7780.6320.9820.0340.0310.0280.020-0.1650.1650.027-0.165-0.0220.1130.073-0.183-0.0810.9810.0230.9760.958-0.006-0.0070.196
max_deliq_12mts-0.032-0.299-0.0290.1480.2180.2320.3050.2740.2600.2230.309-0.1720.0630.0190.048-0.0250.0950.1050.0440.0850.0990.1190.1420.2700.2490.2370.1350.0000.049-0.0340.3930.0760.1400.2380.1960.2770.3060.1850.3520.3100.2250.2560.1570.2120.5181.0000.8340.5190.297-0.014-0.0160.0230.7700.6460.572-0.027-0.0120.022-0.103-0.088-0.092-0.013-0.0020.0130.3190.2160.5450.1310.1290.1320.1270.175-0.1750.1420.1750.1500.2020.1800.1930.1370.5130.0530.4710.4230.055-0.2370.366
max_deliq_6mts-0.045-0.390-0.0840.1530.2440.2660.3380.3080.2900.2560.350-0.1500.0690.0300.052-0.0560.1010.0960.0450.0970.0830.1310.1500.3020.2860.2680.152-0.0110.026-0.0340.4410.0320.1710.1860.2100.2730.3890.2690.3720.3510.2390.2880.1610.2210.3330.8341.0000.3330.328-0.020-0.0160.0210.5320.3310.639-0.028-0.0110.019-0.150-0.132-0.134-0.032-0.0110.0030.1960.1170.3630.1570.1560.1580.1550.257-0.2570.1840.2570.2340.1290.1930.3040.2310.3250.0610.3020.2420.065-0.3040.392
max_recent_level_of_deliq0.073-0.0080.2930.1730.0250.1040.1230.0850.0780.0930.057-0.1990.0220.0140.0220.2120.0920.1180.0430.0790.1570.0060.0980.1100.0650.0660.0710.0140.2860.0310.2080.2960.0750.1690.1010.3190.0420.0060.1520.0950.0920.0870.0260.0160.9890.5190.3331.0000.1240.033-0.0040.0000.6520.5490.4830.0010.0060.2350.1270.1020.0860.0500.0200.0220.7230.5800.9710.0340.0300.0270.019-0.1610.1610.023-0.161-0.0220.1100.072-0.181-0.0810.9920.0110.9710.959-0.001-0.0100.196
max_unsec_exposure_inPct0.040-0.4270.115-0.0390.3780.4080.4230.3690.3330.4090.496-0.0200.0800.0110.000-0.0230.0970.0360.0150.0750.2540.3270.5050.5010.4140.3460.5470.003-0.094-0.0240.6590.1610.2620.1950.1910.4740.4930.4040.7960.4180.2800.3510.1300.1560.1240.2970.3280.1241.000-0.000-0.0000.0000.1540.1250.134-0.008-0.0050.0000.1480.1670.1720.0210.0130.0080.0780.0540.1360.2530.2510.2710.2590.217-0.2170.2270.2170.2980.0680.1240.2280.3000.1180.0900.1230.1080.012-0.2660.489
num_dbt0.0340.0180.076-0.0150.000-0.011-0.020-0.018-0.020-0.009-0.0270.0380.0030.0000.0000.0200.0000.0080.0000.0140.0670.000-0.012-0.011-0.011-0.010-0.0170.0030.0330.0160.0080.0280.0060.0070.0040.022-0.012-0.0030.006-0.012-0.009-0.0140.0170.0000.031-0.014-0.0200.033-0.0001.0000.4320.6390.004-0.0050.0030.0660.0570.0000.0270.0060.0060.2070.079-0.0020.0330.0460.031-0.013-0.014-0.004-0.005-0.0220.022-0.002-0.022-0.008-0.004-0.002-0.035-0.0150.0280.0000.0270.025-0.0060.026-0.008
num_dbt_12mts0.0090.0190.029-0.0080.000-0.008-0.008-0.005-0.004-0.007-0.0200.0030.0000.0000.0240.0020.0000.0070.0150.0110.0240.000-0.0080.000-0.004-0.001-0.0060.0030.0090.002-0.001-0.002-0.004-0.0020.001-0.001-0.013-0.007-0.010-0.0090.000-0.0030.0030.000-0.004-0.016-0.016-0.004-0.0000.4321.0000.764-0.008-0.006-0.0040.0310.0670.0000.000-0.010-0.0090.0800.026-0.001-0.004-0.004-0.005-0.005-0.005-0.000-0.0000.005-0.005-0.0120.005-0.008-0.0030.001-0.018-0.009-0.0040.020-0.003-0.003-0.0020.022-0.004
num_dbt_6mts0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0020.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0180.0000.0230.0210.0000.0000.6390.7641.0000.0000.0000.0000.0000.2880.0000.0000.0000.0000.1300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
num_deliq_12mts0.029-0.0360.1210.1160.1490.1580.1670.1270.1090.1500.094-0.0890.0230.0090.1070.0400.0430.1150.0470.0530.1000.0860.1070.1280.0900.0890.0840.0040.1000.0070.1980.1150.0450.1350.0990.1860.075-0.0010.1740.1410.1180.1150.0520.0310.6540.7700.5320.6520.1540.004-0.0080.0001.0000.8480.759-0.002-0.0000.0300.0540.0460.0400.0120.0150.0160.4110.2820.6980.0580.0550.0420.0350.000-0.0000.0370.000-0.0240.1030.085-0.045-0.0500.6430.0240.5990.529-0.017-0.0970.203
num_deliq_6_12mts0.027-0.0010.1270.0990.1220.1330.1420.0990.0860.1250.082-0.0880.0130.0050.1000.0330.0430.1080.0440.0530.0890.0780.0900.1070.0720.0720.0630.0140.0860.0080.1630.1140.0390.1360.0950.1660.034-0.0290.1520.1100.1050.0920.0410.0300.5570.6460.3310.5490.125-0.005-0.0060.0000.8481.0000.436-0.0000.0030.0290.0550.0460.0390.0150.0180.0180.3860.2800.6050.0400.0370.0310.024-0.0200.0200.014-0.020-0.0480.1100.085-0.079-0.0740.5380.0120.5190.463-0.016-0.0610.179
num_deliq_6mts0.036-0.0560.0940.1060.1500.1620.1520.1140.0920.1520.065-0.0300.0290.0260.0890.0280.0390.1100.0390.0490.0810.0730.0980.1150.0790.0760.079-0.0100.0890.0150.1760.0850.0480.0870.0660.1520.0860.0280.1360.1190.0890.0960.0530.0230.4860.5720.6390.4830.1340.003-0.0040.0000.7590.4361.0000.0020.0030.0070.0470.0410.034-0.0020.0100.0020.3280.2180.5330.0580.0560.0420.0360.019-0.0190.0510.0190.0110.0520.051-0.006-0.0090.4700.0150.4420.340-0.013-0.1050.168
num_lss0.0090.0370.051-0.0210.000-0.013-0.006-0.002-0.003-0.012-0.0260.0150.0150.0000.000-0.0110.020-0.0040.010-0.0010.0400.000-0.007-0.006-0.009-0.004-0.008-0.0070.0010.001-0.002-0.006-0.014-0.012-0.008-0.005-0.027-0.024-0.009-0.013-0.005-0.0090.0150.0090.000-0.027-0.0280.001-0.0080.0660.0310.000-0.002-0.0000.0021.0000.4730.7160.007-0.002-0.0030.0990.0560.0250.0080.017-0.0030.0000.0010.001-0.0000.008-0.008-0.0120.008-0.021-0.010-0.007-0.032-0.0230.0010.013-0.004-0.004-0.0040.020-0.008
num_lss_12mts0.0030.0250.024-0.0070.000-0.010-0.006-0.003-0.000-0.010-0.007-0.0010.0000.0000.0000.0040.013-0.0050.0000.0070.0120.000-0.0070.0110.0040.009-0.009-0.0160.0070.0100.0010.006-0.003-0.0050.0010.005-0.018-0.016-0.007-0.0020.002-0.0010.0140.0090.006-0.012-0.0110.006-0.0050.0570.0670.288-0.0000.0030.0030.4731.0000.816-0.007-0.005-0.0070.0290.0300.0550.0110.0160.006-0.008-0.0080.0090.009-0.0040.004-0.012-0.004-0.014-0.0030.003-0.016-0.0140.0060.0270.0040.0040.0030.0200.003
num_lss_6mts0.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0440.0160.0000.0000.0000.0000.0070.0000.0000.0000.0000.0001.0000.0350.0000.0000.0310.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0140.0040.0140.0220.0190.2350.0000.0000.0000.0000.0300.0290.0070.7160.8161.0000.0000.0000.0000.0000.0980.2040.1300.1890.0830.0000.0000.0000.0260.0220.0250.0000.0220.0000.0000.0070.0000.0000.2670.0110.0140.0140.0000.0000.000
num_std0.159-0.0700.3590.0660.0410.013-0.037-0.060-0.0640.003-0.1230.4020.0250.0220.1630.3360.2490.1470.0960.0580.3890.1210.1620.008-0.065-0.0710.108-0.0100.4200.0880.2700.3330.2060.2210.1400.3700.1060.0670.093-0.080-0.062-0.0720.0620.0270.132-0.103-0.1500.1270.1480.0270.0000.0000.0540.0550.0470.007-0.0070.0001.0000.8810.8210.1230.0720.0440.1450.1380.130-0.021-0.023-0.048-0.056-0.1480.1480.262-0.1480.0320.1310.095-0.163-0.0310.1170.0000.1340.117-0.028-0.0220.002
num_std_12mts0.122-0.1240.2420.0520.0340.009-0.027-0.045-0.0490.003-0.1240.3830.0270.0240.1550.2860.2640.1470.0840.0540.3370.1020.1590.024-0.043-0.0480.116-0.0070.3560.0770.2880.2350.2150.2130.1450.3100.1510.0950.084-0.054-0.044-0.0520.0620.0220.104-0.088-0.1320.1020.1670.006-0.0100.0000.0460.0460.041-0.002-0.0050.0000.8811.0000.9480.0810.0530.0350.1170.1090.103-0.020-0.021-0.034-0.040-0.0440.0440.308-0.0440.0630.1380.109-0.0670.0170.0930.0100.1040.091-0.025-0.0740.008
num_std_6mts0.107-0.1450.2030.0470.0410.011-0.020-0.037-0.0410.005-0.1190.3640.0290.0260.1480.2630.2420.1420.0740.0520.3020.1080.1560.027-0.032-0.0370.117-0.0060.3230.0730.2900.1950.2180.1670.1400.2810.1660.1170.082-0.043-0.036-0.0410.0520.0230.087-0.092-0.1340.0860.1720.006-0.0090.0000.0400.0390.034-0.003-0.0070.0000.8210.9481.0000.0630.0410.0320.0990.0910.087-0.015-0.016-0.025-0.031-0.0060.0060.312-0.0060.0880.0930.107-0.0290.0470.0770.0030.0870.075-0.027-0.0950.012
num_sub0.0270.0340.101-0.0040.012-0.030-0.010-0.006-0.001-0.030-0.0320.0490.0100.0000.0000.0180.0140.0000.0060.0070.0980.011-0.010-0.008-0.010-0.002-0.0190.0000.0390.0230.0190.036-0.0010.011-0.0030.037-0.032-0.0160.018-0.0020.0080.0050.0110.0000.051-0.013-0.0320.0500.0210.2070.0800.1300.0120.015-0.0020.0990.0290.0000.1230.0810.0631.0000.5020.2790.0640.0780.045-0.003-0.003-0.002-0.002-0.0260.0260.015-0.026-0.0130.006-0.007-0.063-0.0270.0470.0000.0440.040-0.0130.0380.009
num_sub_12mts0.0060.0210.037-0.0150.000-0.018-0.008-0.0040.002-0.018-0.0140.0130.0000.0000.000-0.0110.015-0.0040.0000.0090.0550.000-0.018-0.014-0.011-0.003-0.0200.000-0.0010.0090.0030.002-0.0040.0100.0030.007-0.025-0.0120.0080.0020.0100.0090.0000.0000.019-0.002-0.0110.0200.0130.0790.0260.0000.0150.0180.0100.0560.0300.0980.0720.0530.0410.5021.0000.5550.0240.0200.019-0.001-0.000-0.005-0.0060.001-0.0010.0020.001-0.0070.0120.004-0.030-0.0130.0210.0000.0170.013-0.0060.0150.006
num_sub_6mts0.0120.0030.014-0.0050.000-0.012-0.006-0.003-0.000-0.011-0.0090.0060.0000.0000.000-0.0090.000-0.0020.0050.0130.0380.011-0.006-0.004-0.0030.002-0.013-0.0010.0030.0150.0040.006-0.0000.0090.0110.009-0.012-0.0020.005-0.001-0.0010.0020.0080.0150.0200.0130.0030.0220.008-0.002-0.0010.0000.0160.0180.0020.0250.0550.2040.0440.0350.0320.2790.5551.0000.0300.0150.020-0.007-0.007-0.002-0.004-0.0020.0020.002-0.0020.0040.0100.012-0.012-0.0010.0230.0000.0160.0150.005-0.0040.005
num_times_30p_dpd0.0720.0010.2720.1140.0410.0660.0690.0340.0300.0510.001-0.1240.0050.0300.0550.2370.1030.0990.0510.0650.1520.0210.0590.0600.0320.0350.0310.0090.2800.0310.1480.2780.0750.1330.0650.2750.0190.0040.0900.0530.0590.0560.0380.0190.7780.3190.1960.7230.0780.033-0.0040.0000.4110.3860.3280.0080.0110.1300.1450.1170.0990.0640.0240.0301.0000.7960.7330.0130.0100.0220.016-0.1720.1720.033-0.172-0.0140.0740.038-0.164-0.0670.6970.0080.7080.641-0.0220.0300.127
num_times_60p_dpd0.0680.0020.2440.0710.0420.0350.0330.0080.0060.017-0.015-0.0830.0090.0280.0430.2260.1040.0710.0450.0600.1450.0270.0360.0370.0150.0160.0110.0120.2480.0320.1160.2450.0760.1110.0440.2350.0130.0050.0630.0320.0390.0370.0280.0200.6320.2160.1170.5800.0540.046-0.0040.0000.2820.2800.2180.0170.0160.1890.1380.1090.0910.0780.0200.0150.7961.0000.5800.0040.0020.0150.011-0.1580.1580.031-0.158-0.0070.0590.021-0.141-0.0510.5500.0110.5600.495-0.0310.0400.089
num_times_delinquent0.075-0.0110.2990.1820.0960.1320.1450.0970.0860.1180.063-0.1870.0120.0370.1050.2060.1250.1240.0710.0840.1590.0880.1090.1200.0720.0720.0790.0120.2860.0330.2220.3010.0780.1790.1080.3280.0450.0070.1660.1050.1000.0960.0600.0210.9820.5450.3630.9710.1360.031-0.0050.0000.6980.6050.533-0.0030.0060.0830.1300.1030.0870.0450.0190.0200.7330.5801.0000.0400.0360.0300.021-0.1570.1570.028-0.157-0.0220.1170.077-0.183-0.0820.9620.0000.9780.945-0.002-0.0210.214
pct_CC_enq_L6m_of_L12m-0.091-0.2540.0120.0490.4780.4630.6010.6850.7590.4690.174-0.1920.0620.0490.032-0.0040.0280.0250.0890.1280.0380.2110.2090.3250.3160.3040.1990.0020.004-0.0970.2610.0920.1360.1340.1410.2030.2690.2880.2920.4140.3490.4220.1060.2190.0340.1310.1570.0340.253-0.013-0.0050.0000.0580.0400.0580.000-0.0080.000-0.021-0.020-0.015-0.003-0.001-0.0070.0130.0040.0401.0000.9990.2610.2570.052-0.0520.1050.0520.2240.0740.1040.1450.2340.0310.2280.0370.030-0.109-0.1730.400
pct_CC_enq_L6m_of_ever-0.093-0.2550.0070.0470.4800.4580.5990.6860.7590.4660.174-0.1950.0610.0500.044-0.0050.0330.0240.0800.1270.0370.2230.2050.3240.3150.3040.1960.0020.002-0.0990.2580.0890.1350.1320.1400.1990.2700.2890.2890.4150.3500.4220.1110.2210.0310.1290.1560.0300.251-0.014-0.0050.0000.0550.0370.0560.001-0.0080.000-0.023-0.021-0.016-0.003-0.000-0.0070.0100.0020.0360.9991.0000.2610.2580.053-0.0530.1050.0530.2240.0730.1040.1490.2370.0280.2220.0330.026-0.109-0.1720.398
pct_PL_enq_L6m_of_L12m-0.103-0.302-0.0540.0160.2040.1480.2930.3100.3080.1590.267-0.3850.0460.0420.023-0.0210.036-0.0050.0720.0770.0390.3270.2650.6430.7430.8580.2710.016-0.045-0.0950.2470.0640.1350.1350.1570.1840.3020.3380.3060.5240.4500.5670.0970.2200.0280.1320.1580.0270.271-0.004-0.0000.0000.0420.0310.0420.0010.0090.000-0.048-0.034-0.025-0.002-0.005-0.0020.0220.0150.0300.2610.2611.0000.9940.068-0.0680.0920.0680.2860.0970.1350.2120.3070.0250.2120.0280.021-0.150-0.1240.456
pct_PL_enq_L6m_of_ever-0.112-0.302-0.0700.0110.2440.1350.2850.3060.3050.1480.261-0.3930.0490.0400.042-0.0260.059-0.0110.0920.0720.0350.3730.2470.6350.7450.8580.2540.017-0.051-0.0990.2370.0490.1330.1260.1500.1710.3010.3380.2940.5250.4500.5680.1030.2190.0200.1270.1550.0190.259-0.005-0.0000.0000.0350.0240.036-0.0000.0090.026-0.056-0.040-0.031-0.002-0.006-0.0040.0160.0110.0210.2570.2580.9941.0000.076-0.0760.0920.0760.2880.0930.1300.2230.3120.0170.2060.0200.013-0.150-0.1210.448
pct_active_tl-0.191-0.248-0.532-0.0700.2660.0660.0780.0940.0890.081-0.013-0.2080.0410.0360.114-0.3710.4460.0090.150-0.021-0.1090.307-0.0620.0350.0980.0910.0190.000-0.375-0.0890.286-0.7740.101-0.450-0.336-0.3220.1620.141-0.0030.1530.0910.1200.0770.083-0.1650.1750.257-0.1610.217-0.0220.0050.0000.000-0.0200.0190.008-0.0040.022-0.148-0.044-0.006-0.0260.001-0.002-0.172-0.158-0.1570.0520.0530.0680.0761.000-1.0000.3161.0000.187-0.478-0.3480.4740.267-0.1530.082-0.180-0.174-0.010-0.3330.018
pct_closed_tl0.1910.2480.5320.0700.257-0.066-0.078-0.094-0.089-0.0810.0130.2080.0390.0360.1140.3710.453-0.0090.1530.0210.1090.3110.062-0.035-0.098-0.091-0.019-0.0000.3750.089-0.2860.774-0.1010.4500.3360.322-0.162-0.1410.003-0.153-0.091-0.1200.0770.0840.165-0.175-0.2570.161-0.2170.022-0.0050.000-0.0000.020-0.019-0.0080.0040.0250.1480.0440.0060.026-0.0010.0020.1720.1580.157-0.052-0.053-0.068-0.076-1.0001.000-0.316-1.000-0.1870.4780.348-0.474-0.2670.1530.0770.1800.1740.0100.333-0.018
pct_currentBal_all_TL-0.052-0.502-0.0980.0230.0000.1320.1000.0900.0740.156-0.0610.0300.0000.0000.0000.1620.0130.1420.0000.0570.2270.0110.1390.0960.0960.0830.174-0.0100.226-0.0280.4150.0080.4070.0820.0490.2440.4170.3770.1030.1880.1170.1680.0000.0130.0270.1420.1840.0230.227-0.002-0.0120.0000.0370.0140.051-0.012-0.0120.0000.2620.3080.3120.0150.0020.0020.0330.0310.0280.1050.1050.0920.0920.316-0.3161.0000.3160.395-0.0080.0050.3730.3870.0160.0000.0200.014-0.123-0.3060.132
pct_of_active_TLs_ever-0.191-0.248-0.532-0.0700.2660.0660.0780.0940.0890.081-0.013-0.2080.0410.0360.114-0.3710.4460.0090.150-0.021-0.1090.307-0.0620.0350.0980.0910.0190.000-0.375-0.0890.286-0.7740.101-0.450-0.336-0.3220.1620.141-0.0030.1530.0910.1200.0770.083-0.1650.1750.257-0.1610.217-0.0220.0050.0000.000-0.0200.0190.008-0.0040.022-0.148-0.044-0.006-0.0260.001-0.002-0.172-0.158-0.1570.0520.0530.0680.0761.000-1.0000.3161.0000.187-0.478-0.3480.4740.267-0.1530.082-0.180-0.174-0.010-0.3330.018
pct_opened_TLs_L6m_of_L12m-0.090-0.815-0.146-0.0110.2330.1330.1860.2080.2270.1370.269-0.1600.0290.0350.0270.0720.1460.0000.0550.0540.1540.2540.1600.2420.2650.2900.1720.0020.054-0.0650.4210.0530.4040.1560.1930.2830.5760.9120.3100.3670.2880.4280.0600.087-0.0220.1500.234-0.0220.298-0.008-0.0080.000-0.024-0.0480.011-0.021-0.0140.0000.0320.0630.088-0.013-0.0070.004-0.014-0.007-0.0220.2240.2240.2860.2880.187-0.1870.3950.1871.0000.0840.1560.4890.958-0.0250.121-0.020-0.024-0.142-0.2660.287
pct_tl_closed_L12M0.014-0.2030.0940.0270.1890.0530.1060.1110.1040.0480.3130.0190.0300.0320.0850.1300.220-0.0090.0670.0280.1530.2510.1670.1750.1500.1280.119-0.0010.135-0.0120.0940.5240.0930.9330.7340.3570.2820.1860.2950.1430.1010.1180.0830.0690.1130.2020.1290.1100.068-0.004-0.0030.0000.1030.1100.052-0.010-0.0030.0000.1310.1380.0930.0060.0120.0100.0740.0590.1170.0740.0730.0970.093-0.4780.478-0.008-0.4780.0841.0000.7640.0170.0680.1060.0660.1090.108-0.008-0.0160.210
pct_tl_closed_L6M0.001-0.2640.0590.0300.1690.0910.1440.1450.1370.0870.346-0.0070.0220.0250.0610.0920.143-0.0100.0480.0470.1650.2210.1820.2150.1900.1710.139-0.0050.100-0.0200.1630.4450.1240.7630.9730.3470.3440.2720.3300.1940.1380.1640.0740.0660.0730.1800.1930.0720.124-0.0020.0010.0000.0850.0850.051-0.0070.0030.0070.0950.1090.107-0.0070.0040.0120.0380.0210.0770.1040.1040.1350.130-0.3480.3480.005-0.3480.1560.7641.0000.0980.1490.0690.0650.0690.069-0.014-0.0850.243
pct_tl_open_L12M-0.205-0.683-0.579-0.1200.2200.0560.1360.2060.1870.0740.261-0.2740.0340.0400.116-0.1440.320-0.0580.171-0.006-0.0300.2580.0280.1890.2750.2390.068-0.004-0.231-0.1290.282-0.3010.2290.0200.090-0.0270.7030.4930.1980.4320.2490.3270.0790.105-0.1830.1930.304-0.1810.228-0.035-0.0180.000-0.045-0.079-0.006-0.032-0.0160.000-0.163-0.067-0.029-0.063-0.030-0.012-0.164-0.141-0.1830.1450.1490.2120.2230.474-0.4740.3730.4740.4890.0170.0981.0000.590-0.1780.091-0.199-0.189-0.129-0.3470.192
pct_tl_open_L6M-0.133-0.815-0.263-0.0460.1980.1200.1850.2230.2400.1300.295-0.2240.0270.0260.0830.0040.206-0.0240.1320.0440.1240.2260.1400.2490.2930.3120.1600.003-0.032-0.0880.414-0.0450.4020.1260.1780.2220.6210.9200.3110.4070.3130.4530.0650.084-0.0810.1370.231-0.0810.300-0.015-0.0090.000-0.050-0.074-0.009-0.023-0.0140.000-0.0310.0170.047-0.027-0.013-0.001-0.067-0.051-0.0820.2340.2370.3070.3120.267-0.2670.3870.2670.9580.0680.1490.5901.000-0.0820.103-0.082-0.082-0.153-0.2880.289
recent_level_of_deliq0.069-0.0020.2810.1670.0280.0940.1150.0810.0750.0850.056-0.2110.0180.0180.0230.1970.0490.1120.0350.0720.1490.0060.0910.1040.0610.0630.0660.0150.2700.0290.1950.2810.0660.1590.0950.3030.0340.0010.1460.0900.0880.0820.0200.0130.9810.5130.3250.9920.1180.028-0.0040.0000.6430.5380.4700.0010.0060.2670.1170.0930.0770.0470.0210.0230.6970.5500.9620.0310.0280.0250.017-0.1530.1530.016-0.153-0.0250.1060.069-0.178-0.0821.0000.0000.9640.9620.002-0.0050.188
response_flag0.0320.0000.0000.0160.1770.1180.0370.0370.0370.1850.0840.1750.0520.0160.0080.0090.0000.0080.0310.0140.0050.1400.0930.0370.0370.0370.1501.0000.0000.0240.1160.0230.0260.0930.0960.0450.1350.1320.1280.0370.0370.0370.0780.0960.0230.0530.0610.0110.0900.0000.0200.0000.0240.0120.0150.0130.0270.0110.0000.0100.0030.0000.0000.0000.0080.0110.0000.2280.2220.2120.2060.0820.0770.0000.0820.1210.0660.0650.0910.1030.0001.0000.0230.0230.0370.0280.037
time_since_first_deliquency0.078-0.0040.3150.1840.1200.1200.1320.0860.0790.1080.054-0.2030.0310.0260.1130.2300.1700.1150.0620.0850.1530.0950.1030.1110.0650.0660.0760.0140.3050.0360.2100.3200.0770.1720.1010.3340.0360.0090.1530.0900.0890.0840.1020.0710.9760.4710.3020.9710.1230.027-0.0030.0000.5990.5190.442-0.0040.0040.0140.1340.1040.0870.0440.0170.0160.7080.5600.9780.0370.0330.0280.020-0.1800.1800.020-0.180-0.0200.1090.069-0.199-0.0820.9640.0231.0000.9760.004-0.0030.201
time_since_recent_deliquency0.064-0.0010.2870.1640.1200.0950.1110.0760.0700.0860.054-0.2300.0310.0260.1130.2180.1700.0970.0620.0710.1390.0950.0860.0960.0560.0560.0630.0180.2830.0260.1880.2990.0660.1620.0970.3110.0320.0050.1400.0800.0800.0740.1020.0710.9580.4230.2420.9590.1080.025-0.0030.0000.5290.4630.340-0.0040.0040.0140.1170.0910.0750.0400.0130.0150.6410.4950.9450.0300.0260.0210.013-0.1740.1740.014-0.174-0.0240.1080.069-0.189-0.0820.9620.0230.9761.0000.0050.0050.178
time_since_recent_enq0.0620.1410.0750.1360.1490.0130.2440.2480.2800.0020.0280.2980.0850.0450.018-0.1040.1290.0290.0350.034-0.0610.1360.0050.1560.1360.144-0.003-0.011-0.0290.070-0.018-0.007-0.102-0.020-0.020-0.020-0.112-0.1370.021-0.164-0.252-0.2220.3360.441-0.0060.0550.065-0.0010.012-0.006-0.0020.000-0.017-0.016-0.013-0.0040.0030.000-0.028-0.025-0.027-0.013-0.0060.005-0.022-0.031-0.002-0.109-0.109-0.150-0.150-0.0100.010-0.123-0.010-0.142-0.008-0.014-0.129-0.1530.0020.0370.0040.0051.0000.093-0.018
time_since_recent_payment0.0990.3970.209-0.0830.089-0.267-0.230-0.201-0.177-0.269-0.1380.1030.0410.0040.0310.0460.010-0.0480.020-0.067-0.0560.091-0.139-0.170-0.159-0.150-0.158-0.0020.0060.050-0.3410.100-0.199-0.074-0.111-0.109-0.345-0.286-0.212-0.269-0.179-0.2230.0410.090-0.007-0.237-0.304-0.010-0.2660.0260.0220.000-0.097-0.061-0.1050.0200.0200.000-0.022-0.074-0.0950.0380.015-0.0040.0300.040-0.021-0.173-0.172-0.124-0.121-0.3330.333-0.306-0.333-0.266-0.016-0.085-0.347-0.288-0.0050.028-0.0030.0050.0931.000-0.238
tot_enq-0.028-0.4090.1380.2460.1490.4010.6830.6570.6360.3920.521-0.2740.0850.0450.018-0.0180.1290.0950.0350.2180.1330.1360.3860.7460.6840.6560.3520.0110.089-0.0560.5330.2820.2170.3150.3000.4840.4670.4020.6060.8420.6980.7760.3360.4410.1960.3660.3920.1960.489-0.008-0.0040.0000.2030.1790.168-0.0080.0030.0000.0020.0080.0120.0090.0060.0050.1270.0890.2140.4000.3980.4560.4480.018-0.0180.1320.0180.2870.2100.2430.1920.2890.1880.0370.2010.178-0.018-0.2381.000

Test

AGEAge_Newest_TLAge_Oldest_TLAuto_TLCC_FlagCC_TLCC_enqCC_enq_L12mCC_enq_L6mCC_utilizationConsumer_TLCredit_ScoreEDUCATIONGENDERGL_FlagGold_TLHL_FlagHome_TLMARITALSTATUSNETMONTHLYINCOMEOther_TLPL_FlagPL_TLPL_enqPL_enq_L12mPL_enq_L6mPL_utilizationPROSPECTIDSecured_TLTime_With_Curr_EmprTot_Active_TLTot_Closed_TLTot_Missed_PmntTot_TL_closed_L12MTot_TL_closed_L6MTotal_TLTotal_TL_opened_L12MTotal_TL_opened_L6MUnsecured_TLenq_L12menq_L3menq_L6mfirst_prod_enq2last_prod_enq2max_delinquency_levelmax_deliq_12mtsmax_deliq_6mtsmax_recent_level_of_deliqmax_unsec_exposure_inPctnum_dbtnum_dbt_12mtsnum_dbt_6mtsnum_deliq_12mtsnum_deliq_6_12mtsnum_deliq_6mtsnum_lssnum_lss_12mtsnum_lss_6mtsnum_stdnum_std_12mtsnum_std_6mtsnum_subnum_sub_12mtsnum_sub_6mtsnum_times_30p_dpdnum_times_60p_dpdnum_times_delinquentpct_CC_enq_L6m_of_L12mpct_CC_enq_L6m_of_everpct_PL_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_active_tlpct_closed_tlpct_currentBal_all_TLpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_tl_closed_L12Mpct_tl_closed_L6Mpct_tl_open_L12Mpct_tl_open_L6Mrecent_level_of_deliqresponse_flagtime_since_first_deliquencytime_since_recent_deliquencytime_since_recent_enqtime_since_recent_paymenttot_enq
AGE1.0000.1090.3560.0320.059-0.015-0.058-0.077-0.066-0.034-0.0140.2750.0610.1020.1510.0870.1020.1470.6130.1530.1500.1020.099-0.027-0.095-0.0980.0640.0020.1640.4430.0420.2170.0280.0390.0120.168-0.064-0.0720.059-0.144-0.113-0.1360.0750.0500.074-0.028-0.0480.0750.0420.0240.0030.0000.0320.0340.031-0.0070.0000.0000.1720.1310.1180.0300.0120.0000.0830.0770.074-0.075-0.078-0.106-0.116-0.1860.186-0.036-0.186-0.088-0.003-0.009-0.207-0.1300.0710.0290.0750.0620.0790.105-0.030
Age_Newest_TL0.1091.0000.212-0.0270.009-0.187-0.260-0.288-0.274-0.197-0.4210.1290.0000.0000.000-0.1250.0150.0030.000-0.079-0.2180.009-0.207-0.324-0.341-0.317-0.230-0.016-0.1110.069-0.616-0.134-0.519-0.321-0.329-0.444-0.830-0.846-0.456-0.487-0.342-0.4470.0000.0000.000-0.287-0.374-0.001-0.4150.0330.0200.000-0.0350.003-0.0570.0360.0000.000-0.079-0.142-0.1560.0370.0230.0000.0050.004-0.001-0.233-0.234-0.275-0.276-0.2270.227-0.496-0.227-0.812-0.218-0.278-0.683-0.8130.0050.0000.0060.0110.1420.391-0.391
Age_Oldest_TL0.3560.2121.0000.3100.0090.1430.0780.003-0.0020.113-0.0100.4790.0000.0000.0000.3260.0150.1720.0000.1450.3270.0090.2090.062-0.051-0.0570.1380.0060.5330.1950.1920.6410.0650.2090.1270.526-0.089-0.0640.183-0.113-0.063-0.0920.0000.0000.298-0.024-0.0800.2930.1200.0700.0350.0000.1270.1330.0990.0440.0000.0000.3730.2520.2140.1060.0380.0000.2820.2490.2980.0180.013-0.062-0.078-0.5320.532-0.074-0.532-0.1390.0930.064-0.577-0.2520.2830.0000.3130.2850.0700.2120.135
Auto_TL0.032-0.0270.3101.0000.1100.1070.1640.1500.1540.105-0.0190.1220.0040.0320.057-0.0150.0620.0220.0570.086-0.0400.1000.0670.0980.0760.0890.0480.0170.4410.0510.2070.1760.0790.0800.0560.2150.0670.048-0.0200.0950.0740.0840.0670.0230.1820.1650.1790.188-0.040-0.005-0.0110.0000.1250.1050.123-0.0030.0000.0000.0580.0510.0460.001-0.0030.0000.1290.0890.1950.0740.0720.0070.002-0.0580.0580.033-0.058-0.0040.0140.019-0.118-0.0360.1800.0360.1950.1730.135-0.0950.245
CC_Flag0.0590.0090.0090.1101.0000.2940.1410.1410.1410.9490.2000.1000.2080.0720.0790.0000.0000.0880.0120.0220.0140.2250.1520.1410.1410.1410.2161.0000.0000.0370.3250.0200.0320.1580.1540.0710.1970.1720.3420.1410.1410.1410.4710.4420.1060.2130.2380.0600.3780.0000.0000.0000.1500.1140.1530.0000.0000.0000.0220.0200.0000.0090.0000.0000.0610.0550.0920.4710.4750.1820.2120.2730.2700.0000.2730.2090.1890.1650.1920.1950.0550.1690.1060.1060.1410.0750.141
CC_TL-0.015-0.1870.1430.1070.2941.0000.6730.5120.4140.9420.126-0.0040.0640.0220.1010.0010.0220.0880.0190.1720.0220.1550.2450.2910.2190.1860.2190.0070.017-0.0500.3730.1400.0840.1310.1290.2840.2320.1920.4110.2770.1950.2350.1090.0690.0990.2220.2540.0950.399-0.0070.0040.0000.1470.1300.147-0.0150.0000.000-0.005-0.005-0.009-0.031-0.0080.0000.0590.0420.1180.4560.4500.1210.1110.070-0.0700.1300.0700.1250.0440.0800.0480.1150.0860.0860.1060.0830.006-0.2640.384
CC_enq-0.058-0.2600.0780.1640.1410.6731.0000.8970.8310.6510.291-0.1480.0830.0600.024-0.0400.1080.0830.0360.1940.0090.1410.3040.5950.5580.5430.2710.003-0.003-0.0750.3910.1450.1010.1860.1770.3060.2960.2440.4660.5940.5220.5560.3310.4410.1290.3030.3340.1250.409-0.012-0.0000.0000.1610.1450.141-0.0010.0000.000-0.048-0.034-0.035-0.0070.0000.0240.0750.0490.1490.6080.6060.2660.2580.074-0.0740.1030.0740.1720.1120.1370.1240.1750.1170.0360.1350.1160.233-0.2330.674
CC_enq_L12m-0.077-0.2880.0030.1500.1410.5120.8971.0000.9210.5120.302-0.2050.0830.0600.024-0.0590.1080.0530.0360.1570.0010.1410.2580.5910.5890.5800.241-0.003-0.025-0.0760.3470.0930.1050.1740.1770.2520.3270.2680.4120.6570.5740.6150.3310.4410.0870.2690.3010.0870.353-0.0090.0030.0000.1230.1010.1040.0040.0000.000-0.064-0.046-0.046-0.0030.0040.0240.0320.0160.1040.6940.6940.2820.2780.095-0.0950.0940.0950.1930.1150.1440.1990.2140.0810.0360.0910.0810.234-0.2040.647
CC_enq_L6m-0.066-0.274-0.0020.1540.1410.4140.8310.9211.0000.4170.299-0.2180.0830.0600.024-0.0630.1080.0520.0360.144-0.0070.1410.2440.5940.6010.6040.223-0.004-0.026-0.0680.3170.0780.1030.1570.1580.2280.2920.2700.3810.6400.6030.6430.3310.4410.0870.2580.2850.0880.320-0.0090.0050.0000.1080.0920.0850.0080.0000.000-0.065-0.045-0.0460.0030.0050.0240.0360.0170.1020.7670.7670.2860.2840.090-0.0900.0790.0900.2090.1080.1320.1790.2280.0830.0360.0920.0830.263-0.1810.631
CC_utilization-0.034-0.1970.1130.1050.9490.9420.6510.5120.4171.0000.130-0.0260.2070.0740.067-0.0000.0000.0640.0200.1570.0220.2260.2340.2890.2240.1900.2110.0080.012-0.0620.3640.1220.0900.1300.1320.2680.2390.1980.3900.2900.2040.2470.4370.4170.0900.2170.2480.0880.397-0.0060.0040.0000.1420.1260.142-0.0130.0000.000-0.013-0.009-0.015-0.029-0.0070.0000.0460.0280.1090.4620.4580.1280.1190.083-0.0830.1500.0830.1320.0470.0840.0670.1260.0790.1720.0980.077-0.007-0.2610.377
Consumer_TL-0.014-0.421-0.010-0.0190.2000.1260.2910.3020.2990.1301.000-0.1490.0290.0230.005-0.0800.029-0.0260.0260.0810.0910.2340.1900.4430.4040.3520.204-0.007-0.155-0.0530.4650.2620.1930.4010.4030.4310.5060.4020.7550.4530.3090.3830.0740.0700.0560.3120.3480.0590.492-0.021-0.0030.0000.1010.0890.069-0.0180.0000.000-0.141-0.134-0.125-0.047-0.0260.000-0.007-0.0170.0640.1650.1640.2560.253-0.0190.019-0.057-0.0190.2710.3300.3630.2700.2980.0580.0810.0540.0580.021-0.1380.520
Credit_Score0.2750.1290.4790.1220.100-0.004-0.148-0.205-0.218-0.026-0.1491.0000.0260.0310.1990.1710.1960.1210.1800.0320.1990.1480.045-0.235-0.338-0.383-0.002-0.0030.2840.1900.0630.2620.0270.0700.0290.196-0.071-0.113-0.021-0.449-0.514-0.4940.0850.110-0.201-0.163-0.143-0.1980.0060.0240.0050.000-0.082-0.081-0.0290.0040.0000.0000.4020.3880.3720.0540.0180.008-0.116-0.081-0.188-0.172-0.175-0.385-0.394-0.2070.2070.045-0.207-0.1260.0180.005-0.255-0.190-0.2070.136-0.204-0.2300.2930.105-0.270
EDUCATION0.0610.0000.0000.0040.2080.0640.0830.0830.0830.2070.0290.0261.0000.0580.0980.0030.0700.0420.1700.0000.0110.1320.0430.0830.0830.0830.1141.0000.0000.0400.0460.0080.0000.0320.0270.0150.0300.0270.0530.0830.0830.0830.0680.0670.0290.0600.0660.0510.1010.0470.0540.0600.0220.0160.0120.0000.0000.0000.0180.0240.0170.0110.0000.0000.0000.0000.0000.0600.0600.0470.0450.0450.0460.0000.0450.0300.0290.0230.0360.0320.0190.0360.0290.0290.0830.0480.083
GENDER0.1020.0000.0000.0320.0720.0220.0600.0600.0600.0740.0230.0310.0581.0000.0140.0000.0000.0000.1110.0220.0090.0390.0120.0600.0600.0600.0301.0000.0000.0460.0360.0000.0000.0160.0290.0000.0230.0100.0370.0600.0600.0600.0680.0590.0300.0160.0340.0000.0000.0000.0230.0190.0140.0000.0050.0090.0210.0210.0000.0070.0250.0000.0000.0000.0310.0280.0320.0390.0420.0410.0380.0290.0340.0000.0290.0290.0390.0440.0430.0210.0110.0000.0300.0300.0600.0230.060
GL_Flag0.1510.0000.0000.0570.0790.1010.0240.0240.0240.0670.0050.1990.0980.0141.0000.0330.0241.0000.0820.0000.0280.0830.0650.0240.0240.0240.0281.0000.0560.1100.1200.0240.0270.0320.0070.0550.0400.0000.0600.0240.0240.0240.4330.2750.1170.0410.0430.0600.0000.0000.0150.0000.1160.1120.1050.0000.0000.0000.1260.1370.1370.0340.0000.0000.0660.0600.1190.0470.0690.0000.0390.1110.1170.0000.1110.0370.0810.0530.1060.0890.0360.0040.1170.1170.0240.0280.024
Gold_TL0.087-0.1250.326-0.0150.0000.001-0.040-0.059-0.063-0.000-0.0800.1710.0030.0000.0331.0000.1680.0240.0300.0450.1510.0330.031-0.024-0.050-0.0560.0220.0030.6720.0370.2070.5580.2280.2610.1630.5070.1760.140-0.013-0.027-0.026-0.0240.0000.0000.225-0.020-0.0560.216-0.0090.008-0.0060.0000.0470.0380.029-0.0100.0000.0000.3620.3120.2830.048-0.0010.0000.2420.2260.2070.0060.004-0.010-0.015-0.3820.3820.159-0.3820.0780.1380.101-0.1490.0100.2040.0260.2310.222-0.0990.064-0.003
HL_Flag0.1020.0150.0150.0620.0000.0220.1080.1080.1080.0000.0290.1960.0700.0000.0240.1681.0000.0190.0780.0000.1570.0210.0300.1080.1080.1080.0161.0000.2080.0360.2070.2030.1500.1910.1280.2200.1460.1230.0560.1080.1080.1080.0390.0530.1790.0920.1010.0820.0780.0000.0000.0000.0280.0320.0460.0000.0060.0060.2650.2920.2710.0330.0000.0000.1300.0950.1210.0230.0520.0000.0440.4520.4610.0170.4520.1610.2200.1690.3290.2300.0440.0120.1790.1790.1080.0310.108
Home_TL0.1470.0030.1720.0220.0880.0880.0830.0530.0520.064-0.0260.1210.0420.0001.0000.0240.0191.0000.0800.1200.0280.0990.0880.051-0.0010.0050.0300.0010.1870.0970.1410.0860.0620.0340.0050.1310.0260.0020.0470.012-0.0040.0040.2010.1270.1270.1090.0930.1290.0210.0080.0060.0000.1270.1190.1160.0180.0000.0000.1380.1370.1310.0210.0080.0000.1140.0880.1320.0500.048-0.013-0.018-0.0060.0060.141-0.006-0.016-0.002-0.011-0.063-0.0350.1240.0000.1210.1020.030-0.0450.100
MARITALSTATUS0.6130.0000.0000.0570.0120.0190.0360.0360.0360.0200.0260.1800.1700.1110.0820.0300.0780.0801.0000.0000.0520.0480.0410.0360.0360.0360.0371.0000.0360.2700.0640.0420.0000.0320.0190.0490.0000.0000.0520.0360.0360.0360.0890.0920.0560.0400.0440.0290.0160.0130.0050.0000.0520.0490.0360.0000.0060.0060.0830.0870.0760.0180.0110.0000.0570.0510.0650.0690.0530.0740.0870.1460.1490.0000.1460.0540.0590.0470.1650.1200.0270.0000.0560.0560.0360.0290.036
NETMONTHLYINCOME0.153-0.0790.1450.0860.0220.1720.1940.1570.1440.1570.0810.0320.0000.0220.0000.0450.0000.1200.0001.0000.0750.0000.1480.1610.1160.0990.1260.0130.1020.2670.1670.1300.0750.0710.0600.1810.1050.0820.1730.1330.1050.1120.0160.0350.0810.0950.1010.0830.0750.0160.0100.0000.0680.0630.062-0.0160.0000.0000.0650.0610.0590.007-0.0060.0000.0690.0660.0870.1150.1130.0550.049-0.0290.0290.071-0.0290.0500.0190.032-0.0120.0370.0760.0000.0850.0690.027-0.0560.215
Other_TL0.150-0.2180.327-0.0400.0140.0220.0090.001-0.0070.0220.0910.1990.0110.0090.0280.1510.1570.0280.0520.0751.0000.0350.0680.0600.0310.0150.0490.0010.3510.0830.4280.3810.3350.2960.2450.4940.3140.2660.3060.0630.0370.0530.0260.0060.1630.0950.0750.1580.2520.0580.0230.0000.1160.0970.0940.0320.0000.0000.4060.3470.3100.0830.0360.0000.1600.1530.1600.0230.0210.0240.022-0.1320.1320.223-0.1320.1550.1700.177-0.0320.1240.1500.0000.1550.136-0.069-0.0460.129
PL_Flag0.1020.0090.0090.1000.2250.1550.1410.1410.1410.2260.2340.1480.1320.0390.0830.0330.0210.0990.0480.0000.0351.0000.3860.1410.1410.1410.8851.0000.0290.0430.3170.0510.0610.2010.2110.0950.2490.2500.3910.1410.1410.1410.2680.2540.0880.1240.1370.0350.3210.0000.0000.0000.0850.0800.0770.0000.0000.0000.1010.1120.0980.0000.0000.0000.0430.0310.0890.2180.2300.3120.3630.3220.3170.0110.3220.2410.2510.2100.2610.2330.0000.1340.0880.0880.1410.0970.141
PL_TL0.099-0.2070.2090.0670.1520.2450.3040.2580.2440.2340.1900.0450.0430.0120.0650.0310.0300.0880.0410.1480.0680.3861.0000.5210.3580.2910.8780.0030.0580.0430.3530.2880.1590.2490.2200.3680.2620.2290.4850.2590.1860.2270.0910.0450.0900.1400.1480.0930.498-0.007-0.0040.0000.1030.0940.0860.0070.0000.0000.1550.1540.148-0.002-0.0120.0000.0590.0410.1040.2140.2110.2390.221-0.0750.0750.131-0.0750.1450.1630.1710.0220.1300.0860.0850.0990.0800.008-0.1350.380
PL_enq-0.027-0.3240.0620.0980.1410.2910.5950.5910.5940.2890.443-0.2350.0830.0600.024-0.0240.1080.0510.0360.1610.0600.1410.5211.0000.8950.8110.481-0.013-0.021-0.0480.4120.2110.1600.2610.2540.3600.3760.3210.5490.6790.5590.6360.3310.4410.0950.2590.2860.0980.484-0.0040.0020.0000.1180.1060.0980.0050.0000.000-0.0000.0160.0170.003-0.0120.0240.0580.0320.1110.3200.3180.6460.6380.021-0.0210.0900.0210.2290.1850.2120.1840.2400.0890.0360.1010.0860.130-0.1720.739
PL_enq_L12m-0.095-0.341-0.0510.0760.1410.2190.5580.5890.6010.2240.404-0.3380.0830.0600.024-0.0500.108-0.0010.0360.1160.0310.1410.3580.8951.0000.8990.363-0.012-0.058-0.0900.3460.1070.1440.2080.2120.2620.3840.3330.4430.7370.6060.6910.3310.4410.0540.2340.2710.0550.394-0.006-0.0010.0000.0770.0650.0650.0050.0000.000-0.069-0.044-0.036-0.008-0.0160.0240.0290.0130.0650.3080.3080.7450.7460.081-0.0810.0880.0810.2500.1570.1850.2650.2810.0490.0360.0560.0480.110-0.1670.675
PL_enq_L6m-0.098-0.317-0.0570.0890.1410.1860.5430.5800.6040.1900.352-0.3830.0830.0600.024-0.0560.1080.0050.0360.0990.0150.1410.2910.8110.8991.0000.286-0.014-0.056-0.0850.3000.0760.1360.1660.1800.2190.3250.3320.3790.6920.6430.7300.3310.4410.0610.2210.2500.0620.321-0.0020.0020.0000.0770.0670.0610.0130.0000.000-0.078-0.053-0.044-0.010-0.0120.0240.0380.0190.0700.2810.2810.8630.8630.080-0.0800.0680.0800.2720.1280.1600.2270.2960.0570.0360.0620.0550.126-0.1550.637
PL_utilization0.064-0.2300.1380.0480.2160.2190.2710.2410.2230.2110.204-0.0020.1140.0300.0280.0220.0160.0300.0370.1260.0490.8850.8780.4810.3630.2861.0000.0080.0220.0190.3620.2070.1740.2050.1840.3210.2770.2370.4350.2670.1820.2270.2290.2400.0590.1320.1440.0610.541-0.008-0.0020.0000.0820.0730.0660.0060.0000.0000.0990.1070.109-0.017-0.0170.0000.0340.0120.0730.1940.1910.2440.2270.002-0.0020.1640.0020.1560.1240.1370.0610.1480.0560.1380.0680.054-0.009-0.1560.351
PROSPECTID0.002-0.0160.0060.0171.0000.0070.003-0.003-0.0040.008-0.007-0.0031.0001.0001.0000.0031.0000.0011.0000.0130.0011.0000.003-0.013-0.012-0.0140.0081.0000.015-0.0000.0070.0040.0180.0030.0020.0090.0130.022-0.0050.0050.0150.0091.0001.000-0.008-0.002-0.004-0.008-0.0010.0100.0011.0000.0020.005-0.0000.0021.0001.0000.0010.0020.0040.003-0.0131.000-0.009-0.010-0.0070.0060.007-0.010-0.0090.002-0.0020.0070.0020.017-0.005-0.0010.0090.018-0.0071.000-0.008-0.009-0.025-0.0170.005
Secured_TL0.164-0.1110.5330.4410.0000.017-0.003-0.025-0.0260.012-0.1550.2840.0000.0000.0560.6720.2080.1870.0360.1020.3510.0290.058-0.021-0.058-0.0560.0220.0151.0000.1110.3210.6220.2680.2830.1780.6200.1840.140-0.094-0.021-0.026-0.0230.0080.0000.2980.0640.0400.294-0.0970.019-0.0050.0000.1210.1070.1060.0030.0000.0000.4350.3700.3350.0470.0100.0000.2910.2500.2900.0160.014-0.044-0.050-0.3870.3870.227-0.3870.0590.1380.104-0.232-0.0240.2800.0180.3050.283-0.0270.0150.098
Time_With_Curr_Empr0.4430.0690.1950.0510.037-0.050-0.075-0.076-0.068-0.062-0.0530.1900.0400.0460.1100.0370.0360.0970.2700.2670.0830.0430.043-0.048-0.090-0.0850.019-0.0000.1111.0000.0180.1010.026-0.004-0.0170.079-0.048-0.044-0.007-0.127-0.106-0.1200.0580.0500.027-0.040-0.0320.029-0.0240.010-0.0050.0000.0040.0010.0250.0180.0000.0000.1110.0960.0910.0190.0120.0700.0390.0390.029-0.081-0.082-0.093-0.097-0.0800.080-0.016-0.080-0.047-0.028-0.030-0.119-0.0720.0260.0000.0300.0190.0800.056-0.056
Tot_Active_TL0.042-0.6160.1920.2070.3250.3730.3910.3470.3170.3640.4650.0630.0460.0360.1200.2070.2070.1410.0640.1670.4280.3170.3530.4120.3460.3000.3620.0070.3210.0181.0000.2840.5300.3200.2870.7400.7320.5980.6620.4320.2830.3640.0450.0660.2020.3950.4410.2030.651-0.0070.0000.0000.2030.1600.185-0.0030.0000.0000.2700.2950.2970.012-0.0070.0000.1440.1120.2130.2600.2570.2240.2160.267-0.2670.4160.2670.4250.1170.1810.2810.4200.1920.1200.1990.177-0.034-0.3330.528
Tot_Closed_TL0.217-0.1340.6410.1760.0200.1400.1450.0930.0780.1220.2620.2620.0080.0000.0240.5580.2030.0860.0420.1300.3810.0510.2880.2110.1070.0760.2070.0040.6220.1010.2841.0000.1950.6550.5170.8010.2570.1930.3800.0930.0640.0760.0000.0000.3110.0940.0450.3030.1650.019-0.0020.0000.1390.1370.100-0.0020.0000.0000.3520.2540.2070.0350.0020.0000.2900.2510.3060.0800.0770.0590.046-0.7800.7800.024-0.7800.0630.5250.449-0.288-0.0300.2890.0000.3210.302-0.0040.1020.288
Tot_Missed_Pmnt0.028-0.5190.0650.0790.0320.0840.1010.1050.1030.0900.1930.0270.0000.0000.0270.2280.1500.0620.0000.0750.3350.0610.1590.1600.1440.1360.1740.0180.2680.0260.5300.1951.0000.2180.2020.4220.5020.5100.2750.2150.1820.2140.0000.0000.0590.1290.1640.0590.260-0.009-0.0200.0000.0340.0230.043-0.0200.0000.0000.2160.2330.2340.008-0.0060.0000.0650.0690.0570.1130.1130.1310.1290.092-0.0920.4000.0920.3960.0960.1380.2340.4000.0510.0460.0570.043-0.095-0.1960.206
Tot_TL_closed_L12M0.039-0.3210.2090.0800.1580.1310.1860.1740.1570.1300.4010.0700.0320.0160.0320.2610.1910.0340.0320.0710.2960.2010.2490.2610.2080.1660.2050.0030.283-0.0040.3200.6550.2181.0000.8050.5650.4480.3230.4300.2190.1460.1810.0310.0460.1930.2510.1930.1900.2010.004-0.0020.0000.1610.1610.1030.0070.0000.0000.2300.2300.171-0.0110.0060.0000.1490.1270.1960.1240.1220.1220.114-0.4490.4490.088-0.4490.1660.9330.7660.0390.1410.1800.0930.1880.179-0.014-0.0750.317
Tot_TL_closed_L6M0.012-0.3290.1270.0560.1540.1290.1770.1770.1580.1320.4030.0290.0270.0290.0070.1630.1280.0050.0190.0600.2450.2110.2200.2540.2120.1800.1840.0020.178-0.0170.2870.5170.2020.8051.0000.4590.4380.3530.4010.2330.1510.1940.0440.0480.1160.2160.2160.1150.184-0.000-0.0020.0000.1300.1250.085-0.0050.0000.0000.1400.1520.141-0.0190.0030.0000.0730.0530.1190.1290.1290.1400.133-0.3360.3360.053-0.3360.1990.7370.9740.1060.1890.1080.0830.1110.107-0.020-0.1170.297
Total_TL0.168-0.4440.5260.2150.0710.2840.3060.2520.2280.2680.4310.1960.0150.0000.0550.5070.2200.1310.0490.1810.4940.0950.3680.3600.2620.2190.3210.0090.6200.0790.7400.8010.4220.5650.4591.0000.5770.4620.6220.3080.2060.2600.0090.0000.3200.2820.2760.3160.4680.006-0.0040.0000.2010.1800.163-0.0050.0000.0000.3820.3220.2900.0340.0000.0000.2760.2340.3220.1950.1920.1660.154-0.3450.3450.252-0.3450.2900.3710.360-0.0220.2320.3010.0430.3250.302-0.027-0.0990.484
Total_TL_opened_L12M-0.064-0.830-0.0890.0670.1970.2320.2960.3270.2920.2390.506-0.0710.0300.0230.0400.1760.1460.0260.0000.1050.3140.2490.2620.3760.3840.3250.2770.0130.184-0.0480.7320.2570.5020.4480.4380.5771.0000.7550.5600.5400.3190.4270.0290.0610.0430.3060.3860.0440.479-0.022-0.0140.0000.0860.0400.102-0.0340.0000.0000.1210.1760.187-0.029-0.0250.0000.0220.0210.0460.2500.2510.2720.2720.140-0.1400.4140.1400.5820.3060.3650.7030.6250.0360.1110.0330.028-0.122-0.3420.452
Total_TL_opened_L6M-0.072-0.846-0.0640.0480.1720.1920.2440.2680.2700.1980.402-0.1130.0270.0100.0000.1400.1230.0020.0000.0820.2660.2500.2290.3210.3330.3320.2370.0220.140-0.0440.5980.1930.5100.3230.3530.4620.7551.0000.4520.4480.3200.4680.0270.0620.0000.1770.2560.0010.393-0.009-0.0080.000-0.001-0.0360.032-0.0220.0000.0000.0920.1270.141-0.018-0.0180.0000.0060.006-0.0010.2610.2620.3110.3120.124-0.1240.3780.1240.9140.1990.2830.4890.922-0.0040.1450.001-0.006-0.140-0.2790.382
Unsecured_TL0.059-0.4560.183-0.0200.3420.4110.4660.4120.3810.3900.755-0.0210.0530.0370.060-0.0130.0560.0470.0520.1730.3060.3910.4850.5490.4430.3790.435-0.005-0.094-0.0070.6620.3800.2750.4300.4010.6220.5600.4521.0000.4680.3240.3990.0720.0830.1480.3510.3670.1460.792-0.0000.0090.0000.1820.1600.141-0.0030.0000.0000.0920.0840.0790.009-0.0080.0000.0850.0680.1610.2830.2800.2790.268-0.0190.0190.112-0.0190.3090.3120.3390.1980.3100.1400.1220.1470.1340.013-0.2060.599
enq_L12m-0.144-0.487-0.1130.0950.1410.2770.5940.6570.6400.2900.453-0.4490.0830.0600.024-0.0270.1080.0120.0360.1330.0630.1410.2590.6790.7370.6920.2670.005-0.021-0.1270.4320.0930.2150.2190.2330.3080.5400.4480.4681.0000.8090.9080.3310.4410.0920.3010.3420.0920.402-0.0060.0070.0000.1300.1040.111-0.0050.0000.000-0.083-0.056-0.046-0.0020.0000.0240.0510.0340.1040.4150.4160.5090.5120.140-0.1400.1890.1400.3550.1520.1990.4280.4010.0860.0360.0870.080-0.187-0.2800.833
enq_L3m-0.113-0.342-0.0630.0740.1410.1950.5220.5740.6030.2040.309-0.5140.0830.0600.024-0.0260.108-0.0040.0360.1050.0370.1410.1860.5590.6060.6430.1820.015-0.026-0.1060.2830.0640.1820.1460.1510.2060.3190.3200.3240.8091.0000.8930.3310.4410.0900.2130.2300.0900.268-0.0010.0090.0000.1090.0950.0900.0070.0000.000-0.067-0.047-0.0430.0030.0040.0240.0530.0430.1000.3470.3470.4470.4470.089-0.0890.1200.0890.2670.1000.1260.2350.2960.0840.0360.0860.080-0.274-0.1920.689
enq_L6m-0.136-0.447-0.0920.0840.1410.2350.5560.6150.6430.2470.383-0.4940.0830.0600.024-0.0240.1080.0040.0360.1120.0530.1410.2270.6360.6910.7300.2270.009-0.023-0.1200.3640.0760.2140.1810.1940.2600.4270.4680.3990.9080.8931.0000.3310.4410.0880.2470.2770.0870.337-0.0010.0060.0000.1080.0870.0920.0040.0000.000-0.074-0.051-0.0440.0030.0050.0240.0550.0400.0980.4160.4170.5630.5640.118-0.1180.1630.1180.4120.1240.1640.3230.4430.0800.0360.0850.077-0.244-0.2370.766
first_prod_enq20.0750.0000.0000.0670.4710.1090.3310.3310.3310.4370.0740.0850.0680.0680.4330.0000.0390.2010.0890.0160.0260.2680.0910.3310.3310.3310.2291.0000.0080.0580.0450.0000.0000.0310.0440.0090.0290.0270.0720.3310.3310.3311.0000.3330.0940.1560.1580.0320.2050.0070.0000.0000.0590.0500.0530.0000.0000.0000.0470.0630.0560.0060.0150.0210.0440.0340.0600.1050.1170.1020.1090.0780.0790.0160.0780.0520.0800.0690.0790.0610.0320.0770.0940.0940.3310.0110.331
last_prod_enq20.0500.0000.0000.0230.4420.0690.4410.4410.4410.4170.0700.1100.0670.0590.2750.0000.0530.1270.0920.0350.0060.2540.0450.4410.4410.4410.2401.0000.0000.0500.0660.0000.0000.0460.0480.0000.0610.0620.0830.4410.4410.4410.3331.0000.0720.2140.2180.0110.2480.0160.0060.0000.0300.0250.0320.0000.0000.0000.0190.0270.0210.0000.0000.0000.0030.0080.0170.2210.2200.2320.2320.0760.0780.0250.0760.0790.0700.0660.0970.0830.0180.0860.0720.0720.4410.0670.441
max_delinquency_level0.0740.0000.2980.1820.1060.0990.1290.0870.0870.0900.056-0.2010.0290.0300.1170.2250.1790.1270.0560.0810.1630.0880.0900.0950.0540.0610.059-0.0080.2980.0270.2020.3110.0590.1930.1160.3200.0430.0000.1480.0920.0900.0880.0940.0721.0000.5270.3450.9880.1020.036-0.0010.0000.6630.5730.4950.0210.0130.0130.1480.1150.0950.0410.0190.0110.7750.6250.9830.0400.0360.0140.006-0.1830.1830.038-0.183-0.0320.1370.089-0.184-0.0880.9820.0140.9770.9600.0040.0050.203
max_deliq_12mts-0.028-0.287-0.0240.1650.2130.2220.3030.2690.2580.2170.312-0.1630.0600.0160.041-0.0200.0920.1090.0400.0950.0950.1240.1400.2590.2340.2210.132-0.0020.064-0.0400.3950.0940.1290.2510.2160.2820.3060.1770.3510.3010.2130.2470.1560.2140.5271.0000.8380.5300.278-0.009-0.0140.0280.7710.6560.577-0.0190.0220.022-0.099-0.079-0.078-0.038-0.0220.0230.3340.2270.5560.1300.1280.1140.1080.155-0.1550.1470.1550.1390.2170.2010.1890.1290.5240.0540.4830.4340.052-0.2290.370
max_deliq_6mts-0.048-0.374-0.0800.1790.2380.2540.3340.3010.2850.2480.348-0.1430.0660.0340.043-0.0560.1010.0930.0440.1010.0750.1370.1480.2860.2710.2500.144-0.0040.040-0.0320.4410.0450.1640.1930.2160.2760.3860.2560.3670.3420.2300.2770.1580.2180.3450.8381.0000.3470.301-0.034-0.0170.0290.5380.3480.645-0.0320.0190.019-0.157-0.132-0.130-0.057-0.0360.0290.2110.1320.3780.1530.1510.1380.1330.240-0.2400.1830.2400.2200.1370.1980.2980.2200.3390.0580.3160.2550.058-0.3000.392
max_recent_level_of_deliq0.075-0.0010.2930.1880.0600.0950.1250.0870.0880.0880.059-0.1980.0510.0000.0600.2160.0820.1290.0290.0830.1580.0350.0930.0980.0550.0620.061-0.0080.2940.0290.2030.3030.0590.1900.1150.3160.0440.0010.1460.0920.0900.0870.0320.0110.9880.5300.3471.0000.1020.037-0.0010.0000.6640.5690.4940.0220.0000.0000.1440.1140.0950.0380.0180.0720.7200.5730.9720.0400.0360.0140.005-0.1760.1760.035-0.176-0.0320.1350.088-0.181-0.0870.9930.0000.9720.9590.0070.0020.203
max_unsec_exposure_inPct0.042-0.4150.120-0.0400.3780.3990.4090.3530.3200.3970.4920.0060.1010.0000.000-0.0090.0780.0210.0160.0750.2520.3210.4980.4840.3940.3210.541-0.001-0.097-0.0240.6510.1650.2600.2010.1840.4680.4790.3930.7920.4020.2680.3370.2050.2480.1020.2780.3010.1021.000-0.0050.0050.0000.1410.1180.1160.0040.0000.0000.1540.1760.1790.0370.0080.0000.0630.0430.1150.2500.2470.2480.2360.203-0.2030.2210.2030.2970.0770.1180.2200.2980.0960.0890.1020.086-0.007-0.2470.478
num_dbt0.0240.0330.070-0.0050.000-0.007-0.012-0.009-0.009-0.006-0.0210.0240.0470.0000.0000.0080.0000.0080.0130.0160.0580.000-0.007-0.004-0.006-0.002-0.0080.0100.0190.010-0.0070.019-0.0090.004-0.0000.006-0.022-0.009-0.000-0.006-0.001-0.0010.0070.0160.036-0.009-0.0340.037-0.0051.0000.5310.6610.0120.017-0.008-0.0020.0000.0000.0280.0020.0010.1160.0290.0000.0360.0520.035-0.010-0.0100.001-0.001-0.0240.024-0.010-0.024-0.014-0.000-0.003-0.042-0.0190.0340.0000.0340.030-0.0090.035-0.006
num_dbt_12mts0.0030.0200.035-0.0110.0000.004-0.0000.0030.0050.004-0.0030.0050.0540.0230.015-0.0060.0000.0060.0050.0100.0230.000-0.0040.002-0.0010.002-0.0020.001-0.005-0.0050.000-0.002-0.020-0.002-0.002-0.004-0.014-0.0080.0090.0070.0090.0060.0000.006-0.001-0.014-0.017-0.0010.0050.5311.0000.866-0.005-0.003-0.000-0.0010.0000.0000.012-0.007-0.0040.0540.0570.0000.0050.012-0.004-0.002-0.002-0.002-0.0030.008-0.008-0.0070.008-0.011-0.003-0.003-0.022-0.012-0.0030.000-0.003-0.0060.0020.0210.007
num_dbt_6mts0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0290.0000.0000.6610.8661.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
num_deliq_12mts0.032-0.0350.1270.1250.1500.1470.1610.1230.1080.1420.101-0.0820.0220.0140.1160.0470.0280.1270.0520.0680.1160.0850.1030.1180.0770.0770.0820.0020.1210.0040.2030.1390.0340.1610.1300.2010.086-0.0010.1820.1300.1090.1080.0590.0300.6630.7710.5380.6640.1410.012-0.0050.0001.0000.8570.7640.0040.0000.0000.0630.0580.0580.0020.0030.0000.4240.2840.7100.0620.0580.0320.022-0.0230.0230.048-0.023-0.0280.1320.117-0.047-0.0530.6540.0630.6110.542-0.023-0.0900.204
num_deliq_6_12mts0.0340.0030.1330.1050.1140.1300.1450.1010.0920.1260.089-0.0810.0160.0000.1120.0380.0320.1190.0490.0630.0970.0800.0940.1060.0650.0670.0730.0050.1070.0010.1600.1370.0230.1610.1250.1800.040-0.0360.1600.1040.0950.0870.0500.0250.5730.6560.3480.5690.1180.017-0.0030.0000.8571.0000.4610.0100.0000.0000.0650.0550.0530.0060.0040.0000.4100.2870.6200.0500.0460.0280.018-0.0470.0470.020-0.047-0.0600.1400.115-0.085-0.0840.5550.0680.5350.478-0.011-0.0570.184
num_deliq_6mts0.031-0.0570.0990.1230.1530.1470.1410.1040.0850.1420.069-0.0290.0120.0050.1050.0290.0460.1160.0360.0620.0940.0770.0860.0980.0650.0610.066-0.0000.1060.0250.1850.1000.0430.1030.0850.1630.1020.0320.1410.1110.0900.0920.0530.0320.4950.5770.6450.4940.116-0.008-0.0000.0000.7640.4611.000-0.0070.0000.0000.0460.0450.044-0.0130.0030.0270.3370.2260.5440.0540.0500.0280.0210.008-0.0080.0620.0080.0120.0690.0700.003-0.0060.4810.0380.4520.352-0.033-0.1030.163
num_lss-0.0070.0360.044-0.0030.000-0.015-0.0010.0040.008-0.013-0.0180.0040.0000.0090.000-0.0100.0000.0180.000-0.0160.0320.0000.0070.0050.0050.0130.0060.0020.0030.018-0.003-0.002-0.0200.007-0.005-0.005-0.034-0.022-0.003-0.0050.0070.0040.0000.0000.021-0.019-0.0320.0220.004-0.002-0.0010.0000.0040.010-0.0071.0000.6450.6450.0260.0130.0020.0540.0770.0000.0320.0220.0190.0100.0110.0180.0160.005-0.005-0.0050.005-0.0210.006-0.007-0.040-0.0250.0200.0000.0180.017-0.0030.0170.002
num_lss_12mts0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0060.0000.0060.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0220.0190.0000.0000.0000.0000.0000.0000.0000.0000.6451.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0360.0130.0130.0000.0000.000
num_lss_6mts0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0060.0000.0060.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0220.0190.0000.0000.0000.0000.0000.0000.0000.0000.6451.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0360.0130.0130.0000.0000.000
num_std0.172-0.0790.3730.0580.022-0.005-0.048-0.064-0.065-0.013-0.1410.4020.0180.0000.1260.3620.2650.1380.0830.0650.4060.1010.155-0.000-0.069-0.0780.0990.0010.4350.1110.2700.3520.2160.2300.1400.3820.1210.0920.092-0.083-0.067-0.0740.0470.0190.148-0.099-0.1570.1440.1540.0280.0120.0000.0630.0650.0460.0260.0000.0001.0000.8800.8140.1270.0570.0000.1570.1530.144-0.007-0.009-0.047-0.055-0.1700.1700.271-0.1700.0480.1410.094-0.162-0.0130.1350.0140.1500.135-0.041-0.001-0.007
num_std_12mts0.131-0.1420.2520.0510.020-0.005-0.034-0.046-0.045-0.009-0.1340.3880.0240.0070.1370.3120.2920.1370.0870.0610.3470.1120.1540.016-0.044-0.0530.1070.0020.3700.0960.2950.2540.2330.2300.1520.3220.1760.1270.084-0.056-0.047-0.0510.0630.0270.115-0.079-0.1320.1140.1760.002-0.0070.0000.0580.0550.0450.0130.0000.0000.8801.0000.9450.0800.0500.0000.1240.1180.1090.002-0.000-0.029-0.036-0.0630.0630.320-0.0630.0860.1530.115-0.0590.0410.1060.0320.1130.100-0.041-0.0600.000
num_std_6mts0.118-0.1560.2140.0460.000-0.009-0.035-0.046-0.046-0.015-0.1250.3720.0170.0250.1370.2830.2710.1310.0760.0590.3100.0980.1480.017-0.036-0.0440.1090.0040.3350.0910.2970.2070.2340.1710.1410.2900.1870.1410.079-0.046-0.043-0.0440.0560.0210.095-0.078-0.1300.0950.1790.001-0.0040.0000.0580.0530.0440.0020.0000.0000.8140.9451.0000.0670.0390.0000.1050.0990.091-0.004-0.006-0.021-0.028-0.0190.0190.323-0.0190.1030.0970.108-0.0220.0630.0880.0000.0920.080-0.040-0.0800.002
num_sub0.0300.0370.1060.0010.009-0.031-0.007-0.0030.003-0.029-0.0470.0540.0110.0000.0340.0480.0330.0210.0180.0070.0830.000-0.0020.003-0.008-0.010-0.0170.0030.0470.0190.0120.0350.008-0.011-0.0190.034-0.029-0.0180.009-0.0020.0030.0030.0060.0000.041-0.038-0.0570.0380.0370.1160.0540.0000.0020.006-0.0130.0540.0000.0000.1270.0800.0671.0000.4820.5800.0520.0750.0400.0080.008-0.007-0.008-0.0210.0210.022-0.021-0.017-0.018-0.021-0.062-0.0320.0370.0270.0390.034-0.0130.0400.001
num_sub_12mts0.0120.0230.038-0.0030.000-0.0080.0000.0040.005-0.007-0.0260.0180.0000.0000.000-0.0010.0000.0080.011-0.0060.0360.000-0.012-0.012-0.016-0.012-0.017-0.0130.0100.012-0.0070.002-0.0060.0060.0030.000-0.025-0.018-0.0080.0000.0040.0050.0150.0000.019-0.022-0.0360.0180.0080.0290.0570.0000.0030.0040.0030.0770.0000.0000.0570.0500.0390.4821.0000.6710.0290.0400.0150.0100.010-0.013-0.0150.004-0.0040.0120.004-0.0160.0050.004-0.031-0.0220.0180.0250.0120.011-0.0060.0170.002
num_sub_6mts0.0000.0000.0000.0000.0000.0000.0240.0240.0240.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0240.0240.0001.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0240.0240.0210.0000.0110.0230.0290.0720.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.5800.6711.0000.0650.0790.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0350.0110.0110.0240.0000.024
num_times_30p_dpd0.0830.0050.2820.1290.0610.0590.0750.0320.0360.046-0.007-0.1160.0000.0310.0660.2420.1300.1140.0570.0690.1600.0430.0590.0580.0290.0380.034-0.0090.2910.0390.1440.2900.0650.1490.0730.2760.0220.0060.0850.0510.0530.0550.0440.0030.7750.3340.2110.7200.0630.0360.0050.0000.4240.4100.3370.0320.0000.0000.1570.1240.1050.0520.0290.0651.0000.7900.7320.0120.0080.0170.010-0.1880.1880.042-0.188-0.0150.0910.047-0.167-0.0660.6970.0310.7080.638-0.0070.0370.135
num_times_60p_dpd0.0770.0040.2490.0890.0550.0420.0490.0160.0170.028-0.017-0.0810.0000.0280.0600.2260.0950.0880.0510.0660.1530.0310.0410.0320.0130.0190.012-0.0100.2500.0390.1120.2510.0690.1270.0530.2340.0210.0060.0680.0340.0430.0400.0340.0080.6250.2270.1320.5730.0430.0520.0120.0000.2840.2870.2260.0220.0000.0000.1530.1180.0990.0750.0400.0790.7901.0000.5750.002-0.0010.0040.000-0.1650.1650.052-0.165-0.0110.0730.029-0.134-0.0500.5460.0130.5560.490-0.0150.0410.097
num_times_delinquent0.074-0.0010.2980.1950.0920.1180.1490.1040.1020.1090.064-0.1880.0000.0320.1190.2070.1210.1320.0650.0870.1600.0890.1040.1110.0650.0700.073-0.0070.2900.0290.2130.3060.0570.1960.1190.3220.046-0.0010.1610.1040.1000.0980.0600.0170.9830.5560.3780.9720.1150.035-0.0040.0000.7100.6200.5440.0190.0000.0000.1440.1090.0910.0400.0150.0090.7320.5751.0000.0520.0470.0190.009-0.1740.1740.038-0.174-0.0340.1390.091-0.182-0.0890.9640.0480.9790.9460.004-0.0100.221
pct_CC_enq_L6m_of_L12m-0.075-0.2330.0180.0740.4710.4560.6080.6940.7670.4620.165-0.1720.0600.0390.0470.0060.0230.0500.0690.1150.0230.2180.2140.3200.3080.2810.1940.0060.016-0.0810.2600.0800.1130.1240.1290.1950.2500.2610.2830.4150.3470.4160.1050.2210.0400.1300.1530.0400.250-0.010-0.0020.0000.0620.0500.0540.0100.0000.000-0.0070.002-0.0040.0080.0100.0000.0120.0020.0521.0000.9990.2310.2280.057-0.0570.1110.0570.2030.0680.0980.1390.2160.0350.2200.0480.038-0.113-0.1750.404
pct_CC_enq_L6m_of_ever-0.078-0.2340.0130.0720.4750.4500.6060.6940.7670.4580.164-0.1750.0600.0420.0690.0040.0520.0480.0530.1130.0210.2300.2110.3180.3080.2810.1910.0070.014-0.0820.2570.0770.1130.1220.1290.1920.2510.2620.2800.4160.3470.4170.1170.2200.0360.1280.1510.0360.247-0.010-0.0020.0000.0580.0460.0500.0110.0000.000-0.009-0.000-0.0060.0080.0100.0000.008-0.0010.0470.9991.0000.2320.2290.059-0.0590.1110.0590.2050.0670.0980.1430.2190.0310.2180.0430.034-0.113-0.1740.402
pct_PL_enq_L6m_of_L12m-0.106-0.275-0.0620.0070.1820.1210.2660.2820.2860.1280.256-0.3850.0470.0410.000-0.0100.000-0.0130.0740.0550.0240.3120.2390.6460.7450.8630.244-0.010-0.044-0.0930.2240.0590.1310.1220.1400.1660.2720.3110.2790.5090.4470.5630.1020.2320.0140.1140.1380.0140.2480.001-0.0020.0000.0320.0280.0280.0180.0000.000-0.047-0.029-0.021-0.007-0.0130.0000.0170.0040.0190.2310.2321.0000.9940.055-0.0550.0740.0550.2680.0920.1240.1980.2870.0100.1760.0150.009-0.159-0.1280.438
pct_PL_enq_L6m_of_ever-0.116-0.276-0.0780.0020.2120.1110.2580.2780.2840.1190.253-0.3940.0450.0380.039-0.0150.044-0.0180.0870.0490.0220.3630.2210.6380.7460.8630.227-0.009-0.050-0.0970.2160.0460.1290.1140.1330.1540.2720.3120.2680.5120.4470.5640.1090.2320.0060.1080.1330.0050.236-0.001-0.0030.0000.0220.0180.0210.0160.0000.000-0.055-0.036-0.028-0.008-0.0150.0000.0100.0000.0090.2280.2290.9941.0000.062-0.0620.0730.0620.2700.0870.1190.2090.2930.0010.1700.0060.002-0.159-0.1240.430
pct_active_tl-0.186-0.227-0.532-0.0580.2730.0700.0740.0950.0900.083-0.019-0.2070.0450.0290.111-0.3820.452-0.0060.146-0.029-0.1320.322-0.0750.0210.0810.0800.0020.002-0.387-0.0800.267-0.7800.092-0.449-0.336-0.3450.1400.124-0.0190.1400.0890.1180.0780.076-0.1830.1550.240-0.1760.203-0.0240.0080.000-0.023-0.0470.0080.0050.0000.000-0.170-0.063-0.019-0.0210.0040.000-0.188-0.165-0.1740.0570.0590.0550.0621.000-1.0000.2911.0000.171-0.475-0.3480.4540.249-0.1700.076-0.195-0.191-0.022-0.3240.006
pct_closed_tl0.1860.2270.5320.0580.270-0.070-0.074-0.095-0.090-0.0830.0190.2070.0460.0340.1170.3820.4610.0060.1490.0290.1320.3170.075-0.021-0.081-0.080-0.002-0.0020.3870.080-0.2670.780-0.0920.4490.3360.345-0.140-0.1240.019-0.140-0.089-0.1180.0790.0780.183-0.155-0.2400.176-0.2030.024-0.0080.0000.0230.047-0.008-0.0050.0000.0000.1700.0630.0190.021-0.0040.0000.1880.1650.174-0.057-0.059-0.055-0.062-1.0001.000-0.291-1.000-0.1710.4750.348-0.454-0.2490.1700.0780.1950.1910.0220.324-0.006
pct_currentBal_all_TL-0.036-0.496-0.0740.0330.0000.1300.1030.0940.0790.150-0.0570.0450.0000.0000.0000.1590.0170.1410.0000.0710.2230.0110.1310.0900.0880.0680.1640.0070.227-0.0160.4160.0240.4000.0880.0530.2520.4140.3780.1120.1890.1200.1630.0160.0250.0380.1470.1830.0350.221-0.010-0.0070.0000.0480.0200.062-0.0050.0000.0000.2710.3200.3230.0220.0120.0000.0420.0520.0380.1110.1110.0740.0730.291-0.2911.0000.2910.388-0.0030.0080.3540.3800.0290.0000.0290.020-0.128-0.2860.136
pct_of_active_TLs_ever-0.186-0.227-0.532-0.0580.2730.0700.0740.0950.0900.083-0.019-0.2070.0450.0290.111-0.3820.452-0.0060.146-0.029-0.1320.322-0.0750.0210.0810.0800.0020.002-0.387-0.0800.267-0.7800.092-0.449-0.336-0.3450.1400.124-0.0190.1400.0890.1180.0780.076-0.1830.1550.240-0.1760.203-0.0240.0080.000-0.023-0.0470.0080.0050.0000.000-0.170-0.063-0.019-0.0210.0040.000-0.188-0.165-0.1740.0570.0590.0550.0621.000-1.0000.2911.0000.171-0.475-0.3480.4540.249-0.1700.076-0.195-0.191-0.022-0.3240.006
pct_opened_TLs_L6m_of_L12m-0.088-0.812-0.139-0.0040.2090.1250.1720.1930.2090.1320.271-0.1260.0300.0290.0370.0780.161-0.0160.0540.0500.1550.2410.1450.2290.2500.2720.1560.0170.059-0.0470.4250.0630.3960.1660.1990.2900.5820.9140.3090.3550.2670.4120.0520.079-0.0320.1390.220-0.0320.297-0.014-0.0110.000-0.028-0.0600.012-0.0210.0000.0000.0480.0860.103-0.017-0.0160.000-0.015-0.011-0.0340.2030.2050.2680.2700.171-0.1710.3880.1711.0000.0950.1610.4870.960-0.0350.107-0.031-0.038-0.141-0.2560.273
pct_tl_closed_L12M-0.003-0.2180.0930.0140.1890.0440.1120.1150.1080.0470.3300.0180.0290.0390.0810.1380.220-0.0020.0590.0190.1700.2510.1630.1850.1570.1280.124-0.0050.138-0.0280.1170.5250.0960.9330.7370.3710.3060.1990.3120.1520.1000.1240.0800.0700.1370.2170.1370.1350.077-0.000-0.0030.0000.1320.1400.0690.0060.0000.0000.1410.1530.097-0.0180.0050.0000.0910.0730.1390.0680.0670.0920.087-0.4750.475-0.003-0.4750.0951.0000.7670.0390.0830.1310.0770.1300.1290.006-0.0180.218
pct_tl_closed_L6M-0.009-0.2780.0640.0190.1650.0800.1370.1440.1320.0840.3630.0050.0230.0440.0530.1010.169-0.0110.0470.0320.1770.2100.1710.2120.1850.1600.137-0.0010.104-0.0300.1810.4490.1380.7660.9740.3600.3650.2830.3390.1990.1260.1640.0690.0660.0890.2010.1980.0880.118-0.003-0.0030.0000.1170.1150.070-0.0070.0040.0040.0940.1150.108-0.0210.0040.0000.0470.0290.0910.0980.0980.1240.119-0.3480.3480.008-0.3480.1610.7671.0000.1140.1590.0830.0670.0810.082-0.010-0.0900.244
pct_tl_open_L12M-0.207-0.683-0.577-0.1180.1920.0480.1240.1990.1790.0670.270-0.2550.0360.0430.106-0.1490.329-0.0630.165-0.012-0.0320.2610.0220.1840.2650.2270.0610.009-0.232-0.1190.281-0.2880.2340.0390.106-0.0220.7030.4890.1980.4280.2350.3230.0790.097-0.1840.1890.298-0.1810.220-0.042-0.0220.000-0.047-0.0850.003-0.0400.0000.000-0.162-0.059-0.022-0.062-0.0310.000-0.167-0.134-0.1820.1390.1430.1980.2090.454-0.4540.3540.4540.4870.0390.1141.0000.585-0.1800.086-0.200-0.193-0.134-0.3470.184
pct_tl_open_L6M-0.130-0.813-0.252-0.0360.1950.1150.1750.2140.2280.1260.298-0.1900.0320.0210.0890.0100.230-0.0350.1200.0370.1240.2330.1300.2400.2810.2960.1480.018-0.024-0.0720.420-0.0300.4000.1410.1890.2320.6250.9220.3100.4010.2960.4430.0610.083-0.0880.1290.220-0.0870.298-0.019-0.0120.000-0.053-0.084-0.006-0.0250.0000.000-0.0130.0410.063-0.032-0.0220.000-0.066-0.050-0.0890.2160.2190.2870.2930.249-0.2490.3800.2490.9600.0830.1590.5851.000-0.0880.091-0.090-0.092-0.149-0.2810.279
recent_level_of_deliq0.0710.0050.2830.1800.0550.0860.1170.0810.0830.0790.058-0.2070.0190.0110.0360.2040.0440.1240.0270.0760.1500.0000.0860.0890.0490.0570.056-0.0070.2800.0260.1920.2890.0510.1800.1080.3010.036-0.0040.1400.0860.0840.0800.0320.0180.9820.5240.3390.9930.0960.034-0.0030.0000.6540.5550.4810.0200.0000.0000.1350.1060.0880.0370.0180.0880.6970.5460.9640.0350.0310.0100.001-0.1700.1700.029-0.170-0.0350.1310.083-0.180-0.0881.0000.0000.9670.9630.0100.0080.193
response_flag0.0290.0000.0000.0360.1690.0860.0360.0360.0360.1720.0810.1360.0360.0000.0040.0260.0120.0000.0000.0000.0000.1340.0850.0360.0360.0360.1381.0000.0180.0000.1200.0000.0460.0930.0830.0430.1110.1450.1220.0360.0360.0360.0770.0860.0140.0540.0580.0000.0890.0000.0000.0000.0630.0680.0380.0000.0360.0360.0140.0320.0000.0270.0250.0350.0310.0130.0480.2200.2180.1760.1700.0760.0780.0000.0760.1070.0770.0670.0860.0910.0001.0000.0140.0140.0360.0070.036
time_since_first_deliquency0.0750.0060.3130.1950.1060.1060.1350.0910.0920.0980.054-0.2040.0290.0300.1170.2310.1790.1210.0560.0850.1550.0880.0990.1010.0560.0620.068-0.0080.3050.0300.1990.3210.0570.1880.1110.3250.0330.0010.1470.0870.0860.0850.0940.0720.9770.4830.3160.9720.1020.034-0.0030.0000.6110.5350.4520.0180.0130.0130.1500.1130.0920.0390.0120.0110.7080.5560.9790.0480.0430.0150.006-0.1950.1950.029-0.195-0.0310.1300.081-0.200-0.0900.9670.0141.0000.9760.0110.0090.207
time_since_recent_deliquency0.0620.0110.2850.1730.1060.0830.1160.0810.0830.0770.058-0.2300.0290.0300.1170.2220.1790.1020.0560.0690.1360.0880.0800.0860.0480.0550.054-0.0090.2830.0190.1770.3020.0430.1790.1070.3020.028-0.0060.1340.0800.0800.0770.0940.0720.9600.4340.2550.9590.0860.030-0.0060.0000.5420.4780.3520.0170.0130.0130.1350.1000.0800.0340.0110.0110.6380.4900.9460.0380.0340.0090.002-0.1910.1910.020-0.191-0.0380.1290.082-0.193-0.0920.9630.0140.9761.0000.0130.0200.186
time_since_recent_enq0.0790.1420.0700.1350.1410.0060.2330.2340.263-0.0070.0210.2930.0830.0600.024-0.0990.1080.0300.0360.027-0.0690.1410.0080.1300.1100.126-0.009-0.025-0.0270.080-0.034-0.004-0.095-0.014-0.020-0.027-0.122-0.1400.013-0.187-0.274-0.2440.3310.4410.0040.0520.0580.007-0.007-0.0090.0020.000-0.023-0.011-0.033-0.0030.0000.000-0.041-0.041-0.040-0.013-0.0060.024-0.007-0.0150.004-0.113-0.113-0.159-0.159-0.0220.022-0.128-0.022-0.1410.006-0.010-0.134-0.1490.0100.0360.0110.0131.0000.105-0.031
time_since_recent_payment0.1050.3910.212-0.0950.075-0.264-0.233-0.204-0.181-0.261-0.1380.1050.0480.0230.0280.0640.031-0.0450.029-0.056-0.0460.097-0.135-0.172-0.167-0.155-0.156-0.0170.0150.056-0.3330.102-0.196-0.075-0.117-0.099-0.342-0.279-0.206-0.280-0.192-0.2370.0110.0670.005-0.229-0.3000.002-0.2470.0350.0210.000-0.090-0.057-0.1030.0170.0000.000-0.001-0.060-0.0800.0400.0170.0000.0370.041-0.010-0.175-0.174-0.128-0.124-0.3240.324-0.286-0.324-0.256-0.018-0.090-0.347-0.2810.0080.0070.0090.0200.1051.000-0.244
tot_enq-0.030-0.3910.1350.2450.1410.3840.6740.6470.6310.3770.520-0.2700.0830.0600.024-0.0030.1080.1000.0360.2150.1290.1410.3800.7390.6750.6370.3510.0050.098-0.0560.5280.2880.2060.3170.2970.4840.4520.3820.5990.8330.6890.7660.3310.4410.2030.3700.3920.2030.478-0.0060.0070.0000.2040.1840.1630.0020.0000.000-0.0070.0000.0020.0010.0020.0240.1350.0970.2210.4040.4020.4380.4300.006-0.0060.1360.0060.2730.2180.2440.1840.2790.1930.0360.2070.186-0.031-0.2441.000

Missing values

Train

2026-01-17T22:57:16.793841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.

Test

2026-01-17T22:57:28.927931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.

Train

2026-01-17T22:57:17.223444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Test

2026-01-17T22:57:29.316967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Train

PROSPECTIDcampaign_idresponse_flagdirect_mail_flagtime_since_recent_paymenttime_since_first_deliquencytime_since_recent_deliquencynum_times_delinquentmax_delinquency_levelmax_recent_level_of_deliqnum_deliq_6mtsnum_deliq_12mtsnum_deliq_6_12mtsmax_deliq_6mtsmax_deliq_12mtsnum_times_30p_dpdnum_times_60p_dpdnum_stdnum_std_6mtsnum_std_12mtsnum_subnum_sub_6mtsnum_sub_12mtsnum_dbtnum_dbt_6mtsnum_dbt_12mtsnum_lssnum_lss_6mtsnum_lss_12mtsrecent_level_of_deliqtot_enqCC_enqCC_enq_L6mCC_enq_L12mPL_enqPL_enq_L6mPL_enq_L12mtime_since_recent_enqenq_L12menq_L6menq_L3mMARITALSTATUSEDUCATIONAGEGENDERNETMONTHLYINCOMETime_With_Curr_Emprpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_currentBal_all_TLCC_utilizationCC_FlagPL_utilizationPL_Flagpct_PL_enq_L6m_of_L12mpct_CC_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_CC_enq_L6m_of_evermax_unsec_exposure_inPctHL_FlagGL_Flaglast_prod_enq2first_prod_enq2Credit_ScoreTotal_TLTot_Closed_TLTot_Active_TLTotal_TL_opened_L6MTot_TL_closed_L6Mpct_tl_open_L6Mpct_tl_closed_L6Mpct_active_tlpct_closed_tlTotal_TL_opened_L12MTot_TL_closed_L12Mpct_tl_open_L12Mpct_tl_closed_L12MTot_Missed_PmntAuto_TLCC_TLConsumer_TLGold_TLHome_TLPL_TLSecured_TLUnsecured_TLOther_TLAge_Oldest_TLAge_Newest_TL
21639932464310Y71-99999-999990-999990000000084800000000003000111117220MarriedSSC31M280001861.0001.0000.566-99999.000.84311.0000.01.0000.00.21400PLothers673202100.5000.0001.0000.000100.5000.0000100001110144
18112727174910Y49872592802205910000000000000282000000235200MarriedGRADUATE35F350001260.6670.0000.283-99999.00-99999.00000.0000.00.0000.00.51000ConsumerLoanothers663312010.0000.3330.6670.333311.0000.333100200003188
22472933721310Y29-99999-999990-999990000000000000000000002000000359100MarriedSSC36M250002471.0000.0000.500-99999.00-99999.00000.0000.00.0000.0-99999.00000othersothers688101000.0000.0001.0000.000000.0000.00001000001001515
10845516271010Y47-99999-999990-999990000-99999-9999900444000000000020000003221SingleGRADUATE21M12391.0001.0000.948-99999.00-99999.00000.0000.00.0000.0-99999.00000ConsumerLoanothers678101101.0000.0001.0000.000101.0000.000010000010044
37098355644210Y48-99999-999990-999990000000030000000000005000100332100MarriedOTHERS32F18000260.7140.3330.660-99999.000.97410.0000.00.0000.012.49300ConsumerLoanConsumerLoan682725110.1430.1430.7140.286310.4290.1430105001160456
27695941550610Y-99999-99999-999990-99999000000000000000000000111100051111SingleUNDER GRADUATE23M12000561.0001.0000.000-99999.01-99999.00000.0001.00.0001.00.00000CCCC674101101.0000.0001.0000.000101.0000.000001000001022
24798237195711Y53-99999-999990-9999900000000164100000000000500031348311MarriedGRADUATE33M300001260.9090.7780.782-99999.000.89410.3330.00.3330.03.09100PLothers67711110700.6360.0000.9090.091910.8180.09161080011101181
31400747108710Y50-99999-999990-9999900000000000000000000070000001766MarriedGRADUATE45M258001031.0000.6670.963-99999.001.00010.0000.00.0000.04.52700ConsumerLoanothers652303200.6670.0001.0000.000301.0000.000210100112091
14445521666710Y42-99999-999990-999990000-99999-99999001411000000000020000006222MarriedGRADUATE35F30000660.6671.0001.010-99999.00-99999.00000.0000.00.0000.01.66810ConsumerLoanConsumerLoan683312100.3330.0000.6670.333100.3330.0000000100122542
31968647958610Y46-99999-999990-99999000000000000000000000200000058211SingleSSC21M22500681.0000.0000.842-99999.00-99999.00000.0000.00.0000.0-99999.00000ConsumerLoanothers683101000.0000.0001.0000.000101.0000.000010000010077

Test

PROSPECTIDcampaign_idresponse_flagdirect_mail_flagtime_since_recent_paymenttime_since_first_deliquencytime_since_recent_deliquencynum_times_delinquentmax_delinquency_levelmax_recent_level_of_deliqnum_deliq_6mtsnum_deliq_12mtsnum_deliq_6_12mtsmax_deliq_6mtsmax_deliq_12mtsnum_times_30p_dpdnum_times_60p_dpdnum_stdnum_std_6mtsnum_std_12mtsnum_subnum_sub_6mtsnum_sub_12mtsnum_dbtnum_dbt_6mtsnum_dbt_12mtsnum_lssnum_lss_6mtsnum_lss_12mtsrecent_level_of_deliqtot_enqCC_enqCC_enq_L6mCC_enq_L12mPL_enqPL_enq_L6mPL_enq_L12mtime_since_recent_enqenq_L12menq_L6menq_L3mMARITALSTATUSEDUCATIONAGEGENDERNETMONTHLYINCOMETime_With_Curr_Emprpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_currentBal_all_TLCC_utilizationCC_FlagPL_utilizationPL_Flagpct_PL_enq_L6m_of_L12mpct_CC_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_CC_enq_L6m_of_evermax_unsec_exposure_inPctHL_FlagGL_Flaglast_prod_enq2first_prod_enq2Credit_ScoreTotal_TLTot_Closed_TLTot_Active_TLTotal_TL_opened_L6MTot_TL_closed_L6Mpct_tl_open_L6Mpct_tl_closed_L6Mpct_active_tlpct_closed_tlTotal_TL_opened_L12MTot_TL_closed_L12Mpct_tl_open_L12Mpct_tl_closed_L12MTot_Missed_PmntAuto_TLCC_TLConsumer_TLGold_TLHome_TLPL_TLSecured_TLUnsecured_TLOther_TLAge_Oldest_TLAge_Newest_TL
31255446889510Y96-99999-999990-99999000000003330000000000-99999-99999-99999-99999-99999-99999-99999-99999-99999-99999-99999MarriedOTHERS44F180001380.5000.01.023-99999.0000-99999.00000.00.00.00.0-99999.00000othersothers687211010.0000.5000.5000.500110.5000.5001100000201289
35792753688710Y479-99999-999990-999990000-99999-999990030000000000001000000169110Single12TH25M250001160.0000.00.000-99999.0000-99999.00000.00.00.00.0-99999.00000ConsumerLoanConsumerLoan687110000.0000.0000.0001.000000.0000.00000000001011818
11980217971910Y639121219090000-99999-9999911100000000000901330200031299MarriedGRADUATE33M24500821.0000.00.923-99999.0000-99999.00000.00.00.00.0-99999.00001ConsumerLoanCC626101000.0000.0001.0000.000000.0000.00000000101004242
23604935407310Y300-99999-999990-999990000-99999000900000000000050000001111MarriedSSC30M150001020.0000.00.000-99999.0000-99999.00000.00.00.00.0-99999.00000othersothers681330000.0000.0000.0001.000010.0000.33302010002105217
36504054755910Y54-99999-999990-999990000000000000000000001000000514000MarriedGRADUATE22M200001301.0000.00.676-99999.0000-99999.00000.00.00.00.0-99999.00000othersothers686101000.0000.0001.0000.000000.0000.00001000001001717
494907416210Y28-99999-999990-999990000000000000000000002000200629000Married12TH30M200001891.0000.00.8330.99810.78910.00.00.00.05.04200PLPL689303000.0000.0001.0000.000200.6670.0000110001120199
30490745737510Y1807-99999-999990-999990000-99999-99999000000000000000200000020111MarriedPOST-GRADUATE36F500001250.0000.00.000-99999.0000-99999.00000.00.00.00.0-99999.00000othersAL676110000.0000.0000.0001.000000.0000.00001000001007777
441116605810Y52211117727440272721000000000000272200011183771MarriedSSC54F350001260.5831.00.707-99999.00001.04211.00.01.00.06.37110othersothers6721257220.1670.1670.5830.417230.1670.2501005302482565
21999333005410Y107-99999-999990-999990000000000000000000001000111136110SingleGRADUATE28M25000841.0001.00.600-99999.0000-99999.00001.00.01.00.00.68000PLPL672101101.0000.0001.0000.000101.0000.000000100001055
31866047802310Y77-99999-999990-99999000000000000000000000800044439884SingleGRADUATE21M23000421.0001.00.500-99999.0000-99999.00001.00.01.00.01.00000PLPL653101101.0000.0001.0000.000101.0000.000000100001055

Train

PROSPECTIDcampaign_idresponse_flagdirect_mail_flagtime_since_recent_paymenttime_since_first_deliquencytime_since_recent_deliquencynum_times_delinquentmax_delinquency_levelmax_recent_level_of_deliqnum_deliq_6mtsnum_deliq_12mtsnum_deliq_6_12mtsmax_deliq_6mtsmax_deliq_12mtsnum_times_30p_dpdnum_times_60p_dpdnum_stdnum_std_6mtsnum_std_12mtsnum_subnum_sub_6mtsnum_sub_12mtsnum_dbtnum_dbt_6mtsnum_dbt_12mtsnum_lssnum_lss_6mtsnum_lss_12mtsrecent_level_of_deliqtot_enqCC_enqCC_enq_L6mCC_enq_L12mPL_enqPL_enq_L6mPL_enq_L12mtime_since_recent_enqenq_L12menq_L6menq_L3mMARITALSTATUSEDUCATIONAGEGENDERNETMONTHLYINCOMETime_With_Curr_Emprpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_currentBal_all_TLCC_utilizationCC_FlagPL_utilizationPL_Flagpct_PL_enq_L6m_of_L12mpct_CC_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_CC_enq_L6m_of_evermax_unsec_exposure_inPctHL_FlagGL_Flaglast_prod_enq2first_prod_enq2Credit_ScoreTotal_TLTot_Closed_TLTot_Active_TLTotal_TL_opened_L6MTot_TL_closed_L6Mpct_tl_open_L6Mpct_tl_closed_L6Mpct_active_tlpct_closed_tlTotal_TL_opened_L12MTot_TL_closed_L12Mpct_tl_open_L12Mpct_tl_closed_L12MTot_Missed_PmntAuto_TLCC_TLConsumer_TLGold_TLHome_TLPL_TLSecured_TLUnsecured_TLOther_TLAge_Oldest_TLAge_Newest_TL
31092146639810Y78-99999-999990-999990000000034410000000000030000001462000MarriedUNDER GRADUATE44M300002210.6670.0000.297-99999.0000-99999.00000.0000.00.0000.015.00000othersothers702312000.0000.00.6670.333000.0000.00000000001237423
33153649735610Y71-99999-999990-99999000000000000000000000300000010221Married12TH51M186002201.0001.0000.264-99999.0000-99999.00000.0000.00.0000.0-99999.00000ConsumerLoanAL697202100.5000.01.0000.000100.5000.00001000002011064
31361447047510Y99-99999-999990-9999900000000000000000000010000002111MarriedOTHERS55M18700671.0001.0000.500-99999.0000-99999.00000.0000.00.0000.00.66800ConsumerLoanConsumerLoan670101101.0000.01.0000.000101.0000.000000100001055
37727456581010Y3198332525231252500000000000000252000000831000MarriedSSC38M13500660.5000.0000.000-99999.0000-99999.00000.0000.00.0000.0-99999.00000ConsumerLoanothers691211000.0000.00.5000.500000.0000.00001010001103528
31089246635010Y60-99999-999990-99999000000000000000000000600033390651MarriedSSC39M400001011.0000.8330.752-99999.00000.98411.0000.01.0000.04.16000PLothers670606500.8330.01.0000.000601.0000.000100100106483
27618841431910Y-99999-99999-999990-999990000-99999-999990095900000000001000000581000MarriedSSC40M540002971.0000.0000.895-99999.0000-99999.00000.0000.00.0000.01.85200othersothers698101000.0000.01.0000.000101.0000.000100000001199
37639456451410Y57331101011010100000000000000010232001810181121136MarriedSSC29M18000661.0000.0000.9230.91310.92410.5560.00.5560.012.31200othersCC653202000.0000.01.0000.000100.5000.0000010001020148
28064342098610Y83-99999-999990-9999900000000000000000000050001011411SingleGRADUATE21M30000780.5000.0000.333-99999.0000-99999.00000.0000.00.0000.00.34600ConsumerLoanConsumerLoan666211000.0000.00.5000.500110.5000.5000002000020147
36194154293810Y42-99999-999990-999990000-99999-999990075700000000001000000211100Single12TH22M10000471.0000.0000.836-99999.0000-99999.00000.0000.00.0000.0-99999.00000othersothers682101000.0000.01.0000.000101.0000.000010000010077
34979452457811Y64-99999-999990-999990000000000000000000006211200103110MarriedGRADUATE27M25000250.8890.5000.6050.62410.79910.0001.00.0000.57.69100CCConsumerLoan685918200.2220.00.8890.111410.4440.1112116001180232

Test

PROSPECTIDcampaign_idresponse_flagdirect_mail_flagtime_since_recent_paymenttime_since_first_deliquencytime_since_recent_deliquencynum_times_delinquentmax_delinquency_levelmax_recent_level_of_deliqnum_deliq_6mtsnum_deliq_12mtsnum_deliq_6_12mtsmax_deliq_6mtsmax_deliq_12mtsnum_times_30p_dpdnum_times_60p_dpdnum_stdnum_std_6mtsnum_std_12mtsnum_subnum_sub_6mtsnum_sub_12mtsnum_dbtnum_dbt_6mtsnum_dbt_12mtsnum_lssnum_lss_6mtsnum_lss_12mtsrecent_level_of_deliqtot_enqCC_enqCC_enq_L6mCC_enq_L12mPL_enqPL_enq_L6mPL_enq_L12mtime_since_recent_enqenq_L12menq_L6menq_L3mMARITALSTATUSEDUCATIONAGEGENDERNETMONTHLYINCOMETime_With_Curr_Emprpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_currentBal_all_TLCC_utilizationCC_FlagPL_utilizationPL_Flagpct_PL_enq_L6m_of_L12mpct_CC_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_CC_enq_L6m_of_evermax_unsec_exposure_inPctHL_FlagGL_Flaglast_prod_enq2first_prod_enq2Credit_ScoreTotal_TLTot_Closed_TLTot_Active_TLTotal_TL_opened_L6MTot_TL_closed_L6Mpct_tl_open_L6Mpct_tl_closed_L6Mpct_active_tlpct_closed_tlTotal_TL_opened_L12MTot_TL_closed_L12Mpct_tl_open_L12Mpct_tl_closed_L12MTot_Missed_PmntAuto_TLCC_TLConsumer_TLGold_TLHome_TLPL_TLSecured_TLUnsecured_TLOther_TLAge_Oldest_TLAge_Newest_TL
19500329256410Y1312-99999-999990-9999900000000000000000000050003333333MarriedSSC39M15000620.6671.00.707-99999.0000-99999.001.00.01.0000.01.06100PLAL677312200.6670.0000.6670.333200.6670.0002101000121682
12125718190410Y103-99999-999990-9999900000000000000000000020000004111MarriedUNDER GRADUATE40F15000711.0000.00.681-99999.0000-99999.000.00.00.0000.0-99999.00000ConsumerLoanothers675101000.0000.0001.0000.000000.0000.00001000001001313
27231040852510Y104-99999-999990-999990000000000000000000001111110131033MarriedSSC29M70000880.6670.00.975-99999.0000-99999.000.01.00.0001.0-99999.00000ConsumerLoanConsumerLoan669312010.0000.3330.6670.333210.6670.3331201000210146
35568753350210Y46-99999-999990-99999000000000000000000000162222111413135Married12TH27M270001370.8571.00.3200.1171-99999.001.01.00.5001.03.73500CCothers659716510.7140.1430.8570.143510.7140.1430015000071163
18180827277310Y-99999-99999-999990-999990000-99999-99999002641000000000004000000221400SingleGRADUATE22F18000400.6001.01.000-99999.0000-99999.000.00.00.0000.0-99999.00010othersothers708523200.4000.0000.6000.400200.4000.0003000500500452
35054652565710Y-99999-99999-999990-9999900000000000000000000040001117442SingleSSC28M15000641.0001.00.733-99999.0000-99999.001.00.01.0000.01.39900PLothers666101101.0000.0001.0000.000101.0000.000100100001022
453316787910Y78-99999-999990-99999000000000000000000000700000047633Married12TH35M15000261.0000.00.166-99999.0000-99999.000.00.00.0000.02.36700ConsumerLoanConsumerLoan655101000.0000.0001.0000.000101.0000.000000100001099
16618824931910Y993-99999-999990-9999900000000000000000000010000001259000MarriedSSC51M12500390.0000.00.000-99999.0000-99999.000.00.00.0000.0-99999.00000ConsumerLoanConsumerLoan697110000.0000.0000.0001.000000.0000.00000010000104242
454576807310Y76-99999-999990-9999900000000000000000000011000212158310Married12TH38M250001300.6000.00.851-99999.0000-99999.000.50.00.5000.01.59300PLothers689523000.0000.0000.6000.400200.4000.0002001000324586
25611138425310Y292413433000000030000000000038300311180110Married12TH29M35000781.0000.00.3780.5381-99999.001.00.00.3330.03.19400PLothers676404000.0000.0001.0000.000100.2500.00001300001307111